{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "data_folder = os.path.join(os.path.expanduser(\"~\"), \"data\", \"datasets\")\n",
    "adult_filename = os.path.join(data_folder, \"adult.data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "adult = pd.read_csv(adult_filename, header=None, names=[\"Age\", \"Work-Class\", \"fnlwgt\", \"Education\",\n",
    "                                                        \"Education-Num\", \"Marital-Status\", \"Occupation\",\n",
    "                                                        \"Relationship\", \"Race\", \"Sex\", \"Capital-gain\",\n",
    "                                                        \"Capital-loss\", \"Hours-per-week\", \"Native-Country\",\n",
    "                                                        \"Earnings-Raw\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adult.dropna(how='all', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Work-Class</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>Education</th>\n",
       "      <th>Education-Num</th>\n",
       "      <th>Marital-Status</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Capital-gain</th>\n",
       "      <th>Capital-loss</th>\n",
       "      <th>Hours-per-week</th>\n",
       "      <th>Native-Country</th>\n",
       "      <th>Earnings-Raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age         Work-Class  fnlwgt   Education  Education-Num  \\\n",
       "0   39          State-gov   77516   Bachelors             13   \n",
       "1   50   Self-emp-not-inc   83311   Bachelors             13   \n",
       "2   38            Private  215646     HS-grad              9   \n",
       "3   53            Private  234721        11th              7   \n",
       "4   28            Private  338409   Bachelors             13   \n",
       "\n",
       "        Marital-Status          Occupation    Relationship    Race      Sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family   White     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband   White     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family   White     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband   Black     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Black   Female   \n",
       "\n",
       "   Capital-gain  Capital-loss  Hours-per-week  Native-Country Earnings-Raw  \n",
       "0          2174             0              40   United-States        <=50K  \n",
       "1             0             0              13   United-States        <=50K  \n",
       "2             0             0              40   United-States        <=50K  \n",
       "3             0             0              40   United-States        <=50K  \n",
       "4             0             0              40            Cuba        <=50K  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Work-Class', 'fnlwgt', 'Education', 'Education-Num',\n",
       "       'Marital-Status', 'Occupation', 'Relationship', 'Race', 'Sex',\n",
       "       'Capital-gain', 'Capital-loss', 'Hours-per-week', 'Native-Country',\n",
       "       'Earnings-Raw'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    32561.000000\n",
       "mean        40.437456\n",
       "std         12.347429\n",
       "min          1.000000\n",
       "25%         40.000000\n",
       "50%         40.000000\n",
       "75%         45.000000\n",
       "max         99.000000\n",
       "Name: Hours-per-week, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult[\"Hours-per-week\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult[\"Education-Num\"].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' State-gov', ' Self-emp-not-inc', ' Private', ' Federal-gov',\n",
       "       ' Local-gov', ' ?', ' Self-emp-inc', ' Without-pay', ' Never-worked'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult[\"Work-Class\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8ff8d7320>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b7tpERERERHojrD3ThmFEA48Db1/08HeBnzocjtXASeDWc+0eAi4B1gL3G4aRGM7aRERE\nBpuWVhcut2egyxCRHgh3z7QTuAr4+kWPrQFuP/f1a8CDwHFgp8PhaAQwDGMzsBz4R5jrExERGVDl\ntS2s21PIloMlNDldAEwbk8DaeWNYMD0Ni1kjMkUGs7CGaYfD4QFaDcO4+OEYh8PRfu7rciADSAcq\nLmpTce5xERGRYWv/iUqe/Osh2lyde6OPF9ZxvLCOzQeTufu6OdhtlgGqUEQCCfuY6QBMPXz8gqSk\naKxWfbiIiMjQdLKwll/89RDtLt/DOg6frub3b5/ga59d2I+ViUhPDESYbjAMw+5wOFqBLKAIKKZz\nT3QWsM3fTmpqmsNXoYiISJj94Y2jfoP0eZvzirnqWCkZKTH9UJWIdCctLc7ntoEYiPU28IlzX38C\neAPYCSwwDCPeMIxYYBmwaQBqExERCbv65jb2OCoCNzxnw/7iMFYjIr0R1p5pwzDmAY8C44F2wzCu\nB24GnjcM43bgLPC8w+FwG4bxdeBNwAM87HA4GsJZm4iIyECpqGnB7fEG3b64simM1YhIb4T7BsS9\ndEx190FXdNP2T8CfwlmPiIjIYGAyBbw1qBOzuWftRaT/aL4dERGRfpaREt2jGTrGp/serykiA0th\nWkREpJ9F2a0smZUeVFuzycTqnMwwVyQioVKYFhERGQBXLRlPTGTg0ZaXzh9DcnxkP1QkIqFQmBYR\nERkAoxKjeODGHOJjIny2WZ2TyY2XTunHqkSkp0xeb/B3Ew8mFRUNQ7NwERGRizjbXGw7XMbmA8VU\n1bdis5iZMSGJS+ZlMWF0/ECXJyJAWlqcz7uAFaZFRERERPzwF6Y1zENEREREJEQK0yIiIiIiIVKY\nFhEREREJkcK0iIiIiEiIFKZFREREREKkMC0iIiIiEiKFaRERERGREClMi4iIiIiESGFaRERERCRE\nCtMiIiIiIiFSmBYRERERCZHCtIiIiIhIiBSmRURERERCpDAtIiIiIhIihWkRERERkRApTIuIiIiI\nhEhhWkREREQkRArTIiIiIiIhUpgWEREREQmRwrSIiIiISIgUpkVEREREQqQwLSIiIiISIoVpERER\nEZEQKUyLiIiIiIRIYVpEREREJEQK0yIiIiIiIVKYFhEREREJkcK0iIiIiEiIFKZFREREREKkMC0i\nIiIiEiKFaRERERGREClMi4iIiIiESGFaRERERCRECtMiIiIiIiFSmBYRERERCZHCtIiIiIhIiBSm\nRURERERCpDAtIiIiIhIihWkRERERkRApTIuIiIiIhMg60AWIiIiEU11rAxUtlZhNZjJi0omyRg50\nSSIyjChMi4jIsHS67ixvnX2Xg1VH8Xg9AERYIliYnsMV49eSGpUywBWKyHBg8nq9A11DSCoqGoZm\n4SIiEnZ7yvJ4/siLuL3ubrfH2KK5J+cLjI3L6ufKRGQoSkuLM/napjHTIiIyrJQ2lfEbP0EaoKm9\nmV/kPUubu70fKxOR4UhhWkREhpV3C7fi8hOkz6tra2B32f5+qEhEhjOFaRERGTbcHje7SvcG3X57\nya4wViMiI4HCtIiIDBstLidOd2vQ7Wta68JYjYiMBArTIiIybFjNPZukytbD9iIiH6QwLSIiw0ak\n1c6E+HFBtzeSpoaxGhEZCRSmRURkWFmVtTT4tmOCbysi0h2FaRERGVYWpOcwLWlKwHZrx64gIya9\nHyoSkeFMYVpERIYVi9nCHXNvITdtTvfbTRauGL+WT0y5up8rE5HhSCsgiojIsFXaVM6W4h2UNVdg\nNpmYED+OZZmLiI+IG+jSRGQI8bcCosK0iIiIiIgfWk5cRERERCQMNMGmjDjtLjc7jpSz6UAxlXVO\nLGYTxrhELpk3hokZ8QNdnsig5/V6OVZzgk1F28mvLwQgIyadFVmLmZM6E7NJ/TTSt07UnGRT0XZO\n1+fj8XpIj05jeeZictJmYzFbOrWtdtawuWgH+ysO4XQ5ibFFMz89h+WZi4iLiB2gn2DoOll7hk1F\n2zhVdwa310NaVArLMxeTO2pOj+d1H640zENGlKLKJn78xzyq6p3dbl8+ezS3fHg6FrPCgEh3WlxO\nnj74Gxw173W7fVxcFnfMvZUEu8YkS++1udt49vAfOFh5pNvto2PSuSv7VpIjkwDYWLiNl0/8FY/X\n06WtzWzjczM/Re6o7m9Mlc7aPS6eP/Ii+8oPdLs9LSqFu7I/T1p0Sj9XNjAG1ZhpwzBigN8ASUAE\n8F3gCPBbOoadlACfcTgc7f72ozAtPVXT0Mp3n99FXWOb33arczL53Iem91NVIkOHx+vhp/ue5njt\nSb/tsmIzeHD+3URYbP1UmQxXvzzwPAcqD/ttMyo6la8uuIdDlcd47sgLftuaTWbuyr6N6clarCeQ\nXx/+A7vL9vttkxKZxFcX3kusLaafqho4g23M9C3AMYfDcQnwSeAndATqJxwOx2rgJHDrANQlw9xb\nuwoCBmmAjfuLKatu7oeKRIaWI1WOgEEaoKixhN1l+/qhIhnOTtaeCRikAcqbK9lctIO/nnw9YFuP\n18PfTr7RF+UNawUNRQGDNECVs4ZNhdv6oaLBbSDCdCVw/ppAMlABrAb+du6x14DLBqAuGcbaXR42\nHywJqq0XWL+vKLwFiQxBm4qC/6O5sQdtRbqzsWhr0G3XFWyiprU2qLZnGwoujPWX7vXkd31z8Y5u\nh9WMJP0eph0Ox0vAeMMwTgDvAl8BYi4a1lEOZPR3XTK8VdS20Njid+RQJ2dK6sNYjcjQdLYHAaSw\noXjE/4GV3ulJ4K1va+jRvs82FPS0nBHlTH3wz09ta12Pn//hpt9vwzQM42bgrMPhuMowjDnArz/Q\nxOeYlIslJUVjtVoCNxQBml09G2JvsVpIS9MNVCKdmIL/PfLiJSU1FqtZn9MSmnBOChMdE6HPeD8s\nlp49+UlJ0aTGjNzncyDmNFkO/AvA4XAcNAwjA2gyDMPucDhagSygONBOamo0plWCZ3K7ibCZaWsP\nrqcsJd5ORcXIPtMW+aBRUWk0tDUF1TY1MpmaKn1OS+jSIlMpa6oMqm2kxY7T3Rr0vmM88fqM9yPV\nnkI+wQ13jLJG0d5ooqJ5eD+f/k6+BmLM9HvAEgDDMMYDDcBbwPXntn8C0N0B0qei7FYWzUgPuv2a\nnKwwViMyNC3PXBx02xVZS8JYiYwEPX2/xVijg2o7KioVI2lKqGWNCD157pdkzB/x800PRJj+JTDB\nMIx3gd8BtwMPA58zDGMDHVPmPT8Adckwd+WicUTYAr/lZ09MZlKmFm8R+aB56dmkR6cFbJcQEc+y\nzEX9UJEMZ7NTZzA2LnDHRowtmrVjV3DpuFVB7ffKCZdgMgU1onTEmp48lUkJ4wO2i7JGsmbMin6o\naHDToi0yohw6VcXP/nyI1nZ3t9unjEngy9dnEx05ss+yRXypaqnmif3PUN7S/eX3hIg47sr5PFmx\nuo9ceq+utZ4n9j9DcVNpt9tjbNHcOfdWJiaMA+Alx1/8zgJy9aQr+dCES8NS63DT0NbIz/J+RUFD\n98M9oqyR3D7nFqYmTernygbGoFq0pa8oTEuoKmtbWLe3iE0HimlyugCYmBHH2twxLJ6Zjs2q1Q9F\n/GlxOdlWsotNhdsuhOokeyIrshazPHOxlmyWPtXqbmNHyW42Fm2jpKkM6DhpW5a5mJVZS0iwd76S\neLT6OBsLt3Go6igerweryULOqDmsHrM8qN5WeV+bu52dpXvYWLSNosaO6WXjbLEszVzIqqylJEUm\nDnCF/UdhWqQbXq+XllY3VouJCJtmHBAJhdPVCniJtEYOdCkyArS62/B43URaIgMO1XB73LS6W7Fb\n7Fg0q0yvtbnbcAf53A9H/sK0rmXLiGUymTScQ6SXIq32gS5BRhC7JSLothazhWhzcDclSmARPXju\nRxpdzxYRERERCZHCtIiIiIhIiBSmRURERERCpDAtIiIiIhIi3X0lIiI94va42V9xiM1F28lvKMSL\nl8yY0SzPWsKCUdnYLLaBLhGA1jY3246UsmF/MaVVzZjNMCkjnjW5Y8idmorZPPJmJBCRvqep8URE\nJGiNbU38/MCznK0v6HZ7enQad2V/npSopH6urLPS6mYee2k/lXXObrdPG5PAvdfPJTpycAR/ERnc\n/E2Np2EeIiISFI/X4zdIA5Q1V/BE3tPn5p8eGI0t7Tz6ou8gDXC8sI4n/nSQodqhJCKDh8K0iIgE\n5UDFYb9B+rzy5kp2lu7ph4q6t2F/EVX1voP0ecfyazlytqYfKhKR4UxhWkREgrKpaHtY2vYlr9fL\nhv3FQbd/d19RGKsRkZFAYVpERIJS2Bh8SC1uKsXtcYexmu41t7r8Du/4oIKyxjBWIyIjgcK0iIiM\nWF40ZlpEekdhWkREgpIZMzrotunRo7CYLWGspnvRdivJ8fag22elxoaxGhEZCRSmRUQkKCuyFgfd\ndmXWkjBW4pvJZGJVdmbQ7dfkZoWxGhEZCRSmRUQkKDlpcxgTGziopkQmsyRjfj9U1L01uVkkxkYE\nbDclK4E5k5L7oSIRGc4UpkVEJCgWs4UvZd9KVmyGzzYpkcncnXMbUdaofqyss/joCB64MYekON/D\nPSZmxHPv9XMxmbQK4nBV3lxJfn0hVS2a/lDCSysgiohIj7S729ldnsemom0UNHRMLZcRk86KzMUs\nGj2PSGvkAFfYodnZzuaDpWzYX0RZdQsmE0zMjGdtbhYLp4/CalF/0nDj8XrYUryDjYXbKG4qvfD4\nhPhxrB6zjEWj5w1gdTKU+VsBUWFaRESGPa/Xq17oYc7tcfP0od9wsPKozzbLMhbyb9Ov13tBekzL\niYuIyIim8DT8/fnkP/wGaYCtJbt4O39DP1UkI4XCtIiIiAxpLa4WthTtCKrt+oLNA7KgkAxfCtMi\nIiIypO0uy6PN0x5U27q2eo5UO8JckYwkCtMiIiIypFU7ezZjh2b4kL6kMC0yBLjcnoEuQUagvnjf\nhfO925N9uz0ePEHecO/1evU7N8RYTD1bbdM6AKtzyvBlHegCRKR7pdXNrNtTyLbDpTQ5XVjMJuZM\nSuGS+VnMnpgy0OXJMFVU2cS6vYVsP1xGS6sLq8VE9uRULpmXxYwJwS1wUlDeyDt7Ctl5tAxnmxur\nxcy8aalcMm8M08Ym9qq+/LIG1u0tCmrfjS3tbMorZsP+YsprWzABEzLiWJObxZKZ6dis7wcqj8fL\nvhMVrNtbhCO/Fo/XS0JMBCvmZrA2N4vk+MEx3Z90b1rSJF4/E1xbEyamJk0Oaz0ysmhqPJFBaNuh\nUp7951Hcnu7f5stnj+bfPzIDs2YokD60Ka+Y599w+OzBXZOTyWeuNPzOjPH27gJeeOcEvv60XLFw\nLJ+6dGpI9b21q4AX3zmBrw//Dy0axw2XTAE6Qvf//TGPuqa2btuOT4/j/huziY+OoK3dzc/+fIiD\np6q6bWuPsHDXx2frJHaQ+972H1HaXB6wnZE0hXtzv9gPFclwoqnxRIaQo2eq+dU/fAdpgC2HSnll\n/cl+rEqGuwMnK3nujWN+h0K8u7+Yv2w67XP77mPl/OFt30Ea4M1dBby+/WyP69t5tKwjpPtp88bO\nfN7YkU9dU5vfIA1wtqyBx185gMfr5dl/HvUZpAFa29w88aeDFFY09rhu6T/XT/sYZpP/WBNhieDa\nKR/up4pkpFCYFhlkXtt6Jqixne/sLaSxJbi710UC+duWM35D8Hlv7i6gpdXV7ba/bvEdtC/2z+1n\naXf1bGqyv205E/S+39lT4DdIn3equJ4N+4rYeTRwb2Zbu4fXt+cHVYMMjBnJ07h9zueI9rGUfUJE\nPPfkfJ5xcWP6uTIZ7hSmRQaR0upmjuXXBtW23eVhy8GSMFckI0F+WQOniuuDatva5mb74dIuj58o\nrKWooimofTQ5Xew6FjjAnne8oJbiyuD23djSzrq9RUHv+40dwQfkXcfKaXLqBHYwm506g/9a/i1u\nnv5JZqdMZ3LCBOamzuLfZ/0b31v2DSYlTBjoEmUY0g2IIoNIUQ8vIxcFGTBE/Ak2qJ5X2E37nr4X\ngw3eHW179nvR7Oy+57w7tUH0YJ/ncnsor2lhYoatR/VI/4qwRLAscyHLMhcOdCkyQqhnWmRQ6dkN\nhbr9UPpcQqJ0AAAgAElEQVRED99I3TXv8XuxJ98wiG60HUSliMggoZ5pkUFkfHosJhNBjV0FGJce\nF96CZEQY38P3UbTdxq/+cYQzJQ14vF5GJ0czfXxS2I45YXTP6ouPtlHfHNxwjJR4O6XVLUG1tdss\npCdF96gWERn+1DMtMoikJkYFPf2WPcLCstmjw1yRjAQZKTFMHxfc/M8Ws4m/bzvDloOlFFU2UVLV\nzL4Tlbzw9gkibMH9SUmIiWDetLSg65uYEc/4IAN1QmwEly8YG/S+r142Ieje5iWz0omyqw9KRDpT\nmBYZZK5ZMRGrJfCv5oeXjNcfdukz166chMUcOFX6m7KxrT24VQOvWRnce/xi162aFFTo/fjKSayZ\nl0VaYuBFVmZNSGLp7AxWZ2cGbBsTaeWqxeOCKVVERhiFaZFBZlJmPHdfNwe7zfdytx9aPI6rl03o\nv6Jk2Js2NpE7r51NhLX3fxZ8LSZkAq5fM5k1OVk93uecSSl8/qMzsVp87/uGtVNYlZ1JTKSN/7gx\nh/Sk7qdIA5gxPokvfXwOADdfMY2ls3xf5YmLtvHlG7IZpSEeItINrYAoMkjVN7WxMa+YbYdLqW1s\nw24zM/fcss4aKy3hUtvYysb9xWw7UkZ9UxuRERZypqRSVe/kwEnfC5tcbHRyFAump7PzSBkNLe1E\n2S3Mm5rG2nlZZKTE9Kq+6non7+4vvrDvaLuF3GlprM3tuu92l5udR8t5d38RpVXNmM0mJmbEc8m8\nLGZPSukS+k8U1rJ+bxFHz9bQ7vKQkhDJirkZLJ+dQXSkrgKJjGT+VkBUmBYRkYC+9uRWKmqdQbf/\n+QOriIxQABWR4UHLiYuISK94/IyV7ov2IiJDlboNREQkoFFJ0VTVtwbVNj7a1unm2MLyRjYdKKG6\n3onNambG+CQWz0wnws99ASIiQ4XCtIiIBLQ6J5OjZ2uCarsyOxOTyURDcxtP/e0wh890/r7tR8r4\n4/r3uOmyqSybnRGOckVE+o2GeYiISEDzpqWRkRJ4Notou5W1uVm0tLr43xf2dQnS5zU5XTzz96Ns\nOVjS16WKiPQrhWkREbnA7XHT0NaI09X5ZkOrxcz9n8xmVKLv6eai7VbuvX4uyfGR/GtnPoUVTQGP\n94e3j+Nsc/W6bn88Xi8NzW00OYNbFVEGryZnOw3NbUGNyW9td1Pf1Ea7yx2w7fn3fYsruNUw+5LT\n5aShrRG3J3CdMjhpmIeIiFDUWMK7BVvYXbaPNk9H6JwYP46VWUtZkJ6DxWwhNTGKh25ZwJu7Cnhn\nTyHNzvdDsN1mZtns0aQnReFye9iQVxzUcVta3Ww7XMba3J7PPR1Idb2TdXuL2Hyg+MLy4ulJUazJ\nzWJVdqYWPRoiWlpdbMorZv3+Ysqqm4GOub9XzM3g0nljSI7vvEDP/vcqWb+3iEOnq/B6O1btzJma\nymXzx2CM67zsfVlzBRsKt7KzdA8t504gx8RmsjJrCYtHz8dmsYXlZ3J73Owpz2Nj4TZO158FIMJs\nY0F6DmvGriArVsOfhhJNjSciMsLtKNnD7469jMfb/QqGM5Kn8cU5nyPCYqO8ppkfvbifyrrup8mL\nj7bxqUun8tRrR4I+fu7UVO75xNyQavflvcI6fvJKHk3O7nu9M1Ki+Y8bc7oEMRlcahpa+dGL+yip\nau52e0xkx9WQqWMS8Xq9/OZfDjbs930i97HlE7h25SQADlYe4VeHfk+7p/srFhPjx/Gl7NuItvm+\nGhOKNnc7Tx18nqPVx7vdbjaZuXn69SzJWNCnx5Xe0dR4IiLSrRM1p/wGaYCj1cf5/bGXaXe5eeyP\neT6DNEB9czu/e6v7kOBLmyu4ZciDVV3v9BukAUqqmvnxy3m4PX17bOk7Ho+XH7+c5zNIQ8fY+8df\nOUB1vZPXtp7xG6QB/rblDBvziilqLOFXh37nM0gDnK7P51eHfhdy/b784dgrPoM0gMfr4ffHXuF4\nzck+P7aEh8K0iMgI9ubZ9X6D9Hl7yvJ45+BxymsCjylt9hNiu5MYG9Gj9oGs31fkN0ifV1jRxP4T\nlX16bOk7+9+rpKC8MWC7JqeLN3cV8ObOgqD2+49tZ3j77AbaPYHfI8dqTnCmPj+o/QajsqWK3WX7\nA7bzeD28eXZ9nx1XwkthWkRkhKp21vjtIbuYFy/r87cHvW+rxecV0S5WzOm78aEer5dNQY7XBgL2\nZMrA6clrszGvmObW4E7iKhoa2FOeF/S+txTtCLptIFuLd+EluFGqx6pPUNVS3WfHlvBRmBYRGaHK\nmyuD/sMO0OgJbp5pAJc7uP2OT4/rclNYbzQ7XRduNgxGabXvIQQysEqrA88Gc56zLfiZMEwRLbi9\nwbcva+67qxdlzRVBt/XipbxFV06GAoVpEZERymzq2Z8AE8H3NpuAjy4d77dNSnwkd103u0c1BGIO\nvsSO9j39Buk3ZlO4Xpue7bcv6+jpvsyKaUOCXiURkREqKzYDmzn46eFSbcEPxxg/Oo7rVk/mjmtm\nMTEjvtO2KLuFS+eN4dufnU9qQt/OlBAdaQtqcZnzJmXGB24kA6Inr01Pxt17ndFEWoJ/302IHxd0\n277cl9VsJStOU+QNBZpkU0RkhIqxRTNvVDY7SvcEbGszW/nwtBU8eSi4MdZr53XMG71oRjqLZqRT\nVNlEdb0Tm8XMxIx47BGWXtXuz5qcLF5450RwdYZhfmvpG2vnjWHb4bKg2l6xaBzr9hT6nWnmvEkZ\nSczMWsg7+RsDtjVhYkXWkqBqCMaSjAW8dupffmcROW/+qGxibTF9dmwJH/VMi4iMYB+acAnR1sC9\ndFeMX8vCqVnMmhB4fPP40XEsmZne6bGs1BjmTEph+viksAZpgBVzM8hMDRxCcqemMnVMYlhrkdBN\nyUpg/rS0gO0yUqJZnZ3J9WsmBxzAYTGb+MTqyVw6dhUJEYF7vleNWUpqVHKQFQcWY4vmyvGXBGwX\nZY3iygmB28ngYHn44YcHuoaQNDe3PTzQNYiIDHUxthiMpCkcrDxKq7uty3YTJi4ft4arJ38Ik8nE\nvGlpnC1r8DlF3qTMeL78yWyi7eFZOS4YNquZ3KlpHD1bQ31T158JOoL07R+bhcWiPqXBLHdqKiVV\nzT7nmh6TFssDN2QTHxNBVlosSXF2Dp7qWPnwg+w2C3dcM4s5k1OItNqZlTKdw1XHfC4hviJzMTdM\nuxZTH4/dnpo0CbfHzam6M91uj4uI5a7s27QK4iATE2N/xNc2rYAoIiK0udvYXbaf7SW7qW2tw2a2\nMSNlGiuzlpIe3bV30JFfw/p9RZwpbQAvZKbGsCY3i9mTksN441jPeDxe8t6rZENeMSVVTZhNJiZl\nxrM2dwxTxiQMdHnSA+8V1bF+bxGniuvweL2MTo5hdU4mOVNSu9xEWtPQyqa8YvYcr6DZ6SI22sbi\nGemsmJtBbFTnkzyXx8Xe8gNsK95FlbMaq9nK1MRJrBqzLOxhtqy5gk1F2zhafYJ2dxuJ9gQWZ8xn\nYXouEZa+nXtdes/fCogK0yIiIiIifmg5cRERERGRMFCYFhEREREJkcK0iIiIiEiIFKZFREREREKk\nRVtkxGppdVHX1IbVbCI5IXLQzEAgPdPu8lDT4MQLJMdFYrP67yOornfS2u4mLjqiy539H9TsbKeu\nqQ27zUJyfGQfVj047DteQV1zK5MzEhibHge8/zNHWC2kJPj/mV0eFzXOOrx4SbInYLP4fz5rW+to\ndbUSExFzYTGKmqYmiuuqiLBYmZiWjtXceQ7qtjY3+96roN3lwRiXRFpi1zmx61rrcbqcxNhiiI0I\nbZGL87XFRsQSY+u6guLpknrOltWTGGMnZ2rguY9D0dLqoraxFZvVTEp8ZJ9PyTaUnCgrwVFcRnx0\nJKuM6QC4PR6q6lvxerwkxtmx2/zPV17b2EpLq4uYKBvx0f5nx3C6nNS1NWA1WUiKTMRsMuN0tbH1\nvWO0uNrIzpzAmOTUPvv5hhqP10O1sxaP102CPQH7IJ1tpN3josZZC0BSZGKPVnjtDc3mISPO2dIG\n3txVwK5j5bjcHgBSEyJZk5vF2twsouw6xxwKquudvLW7gM0HSmhyugCIibSyfE4Gly8Y2ykIutwe\nNh8oYd3eIgorGgEwm0zMnZzC5QvGMGNC50UZHPk1vLW7kLz3KnF7Oj5qMlNjWJubxarszICBfTCr\na2zj//64n/zyxk6PW8wmkuPsVDe0XviZM1KiWZObxZqcTGzW94NLXWs97xZuYWvxThrbmwCIskay\naPR8Lhm7gtSolAttPV4P20v2sLFoKwUNRUDH3NWjLZOpb26l0VaEydzxe2hqi8aIyebT866gsdHD\nE38+SEVt5xXtIiMsXLtyIpctGMPO0r1sLNzG2YaCC/s1kqawZuxy5qTODPhcuD1utpfuZlPhNgoa\niy/sY2aKwdoxK5iRMo3nXj/K1oOluDyd/+RMyojj/huyiYnqfag4VVzPm7vy2Xu8Ape74zjpSVEd\nz31uVsDQOJw89c5GduY14WmLgPNLsJg8RCU2Ym6Pp+nc29YeYWHpzHQuXziWjJT3T6C8Xi/bD5ex\nbm8hJ4vrLzw+Y3wSl80fQ+4HFoHJbyhkXf5m9lUcwOXp+ByJ86bSVBeJO7oCU0Rrx349ZkyNKazM\nWsKnFq4M3xMwyDS3N7OhcCubi3dQ21oHQITZxvz0HC4Zu5LM2NEDXGGHGmct6ws2s71kN02ujjnJ\nY6zRLMlYwNqxK0iK7P3iTINyajzDMG4GvgK0A/8JHAR+S8fQkxLgMw6Hw+d6mwrTEoodR8p45u9H\nLoSFD8pKi+HBT+WSEDM4z7qlw9nSBh77434amrv/iIiNsnH/DdlMzIinrd3NT145wNGzNT7394nV\nk/jI0gkAvLWrgBffOYGvD5ipYxK4/4ZsIiOG3klXWXUz33x6e7cLWvgzOSueB27IIcpupbixlCf2\nP01dW0O3baOskdw591YmJ06g3ePimYO/4VDVsU5t3A2JmGPqMJm7L8RTPo7WM/7DcNLoZpzjfC8H\nfem4VVw35aM+t7d7XDx14HmOVDt8tjEdvoLmJt8nTmaziUe/tIyEWLvfWv3ZlFfM82848Ph4UcaP\njuM/bswJeBVlOPjK83+nqiQKfK1jaHYBLvC8f6Jst1m467rZzJ6Ygsfj5anXDrPzaLnPY1w2fwz/\ndvk0AHaV7uO3R/+I2+u+sN3THA1mMEd2v0iM12Mi05XDtz90U49/vqGm2lnD4/ueoqKlqtvtNrOV\nW2fdzNy0Wf1cWWf59YX8LO9XF07sPyjWFsNd2bcxLn5Mr44z6KbGMwwjmY4AvQz4KHAt8F3gpw6H\nYzVwErh1IGqT4et0Sb3fIA1QVNHEE3860I9VSU81O9v5v5fzfAZpgMaWdn78ch6NLe385l8Ov0Ea\n4NUNp9h9rJy89yp5wU+QBjhRWMez/zgaYvUD6z9/taPHQRrgZFHH706ru42f5f3KZ5AGaHE5efLA\nr6lrbeCVE3/rEqQ9zTF+g7TbDa1nZgSsqaY0ivaScT63v5O/kU1F231uf/n4X/wGaadjnt8gDR2L\nwnzjKd/HCOR4Qa3fIA0dJ46//OuhkI8xVDz293f8B2kAjxU+sEBLa7ubn/3pEOW1Lfxp4ym/QRrg\n7T2FvLW7gLP1Bfzm6EudgnQHs88gDWAyeym27ueFnRsC/ERDm8fr4Rd5v/YZpKHjhPTZw7+ntKms\nHyvrrLm9mZ/nPeszSAM0tjfx8wPP0tzu+3XtrYG6VnkZ8JbD4Wh2OBxlDofjdmAN8Nq57a+dayPS\nZ97cVeA3SJ93sqgeR77/8CUDZ/PBUp9LRF+sobmdN3fms/1wcB/0/9x+lte3nw2q7R5HBeU14ftg\nDofNB0tod4d+QW/fiUrecuy+cKnXn2ZXC+8UbGR78a4u27xuq88gDeA6PQe/geoCE67iqX5bvJ2/\nge6uvta3NbC9ZI/f7/XWBTcu2tnm5uCpyqDaftDr28/6DdLnHT5Tw9lS3ycww8Hhoy6Cet1ddoju\n/Pnc2u7mzZ35rNtbGNSx3tyZz1tnN+DxejrvujoVc3Sjj+96n8nsZXPxzqCONVQdrjpGcVNpwHbt\nHhfrCzb3Q0Xd21qyi4b2wK9ZQ1sj20p2h62OoMK0YRhx3Tw2qRfHnQDEGIbxV8MwNhiGcQkQfdGw\njnJAi9JLn2l2trPH4b/H4mIb84rDWI30xqYevDbv7i8OKqwAnClt4Hhh4KAI4AU2HSgJuo7B4E8b\nTvZ6H+/uDy6sAGwp2oHrA71+XpcVc0y9j+/o4KnpwRhMtxV3q+8hWZUtVRyv6fpz7yjZ002P5Pva\niyYRXKDv8Ls3jwfd9ryahlYOnPLd6/dBw/kz6e3Dh/C6ezCMxdN1iNXmgyU423y/pheramogr6Jr\nb7/J4ummdfe8sVWcqQz+b8pQs7WbE2FfdpXto83t+0phOPWkzq1hPAEKtmf6LcMwLtzGahjGp4F3\nenFcE5AMfBz4d+DXdP7kGrm3MEtYVNe3XrixJxjltS1hrEZ6oyevTWNL+D7gy2qG1nvk/E2avdEY\nuAPoAqfb2eUxb3uE317pjkY9uWBqwluf7LdFZTeXqf1dugZwN/bsZiV/Q458qapz9mjIzXD+TDqU\n38NQ6up6AtXWHnwQNtmdeOja3mQLfMXrQluzh32Fp4NuP9R093vjS6u7jYa2Hnw49KGe1NmTtj0V\n7B00DwFvGIbx73TcNDgO6M3trGXAVofD4QFOGYbRALQbhmF3OBytQBbg9zQ8KSkaq3Xk3OEsveMM\n/nMWgEi7jbS0LhdkZBCwWsy0u4J7Qc1mE54ghvaEIjY6Yki9R8zm3vdRmHs9MDAMr4XFf29kYkJM\nl9cpNt//lH8mk6dHlVosph6/F2paenZyExU5fD+T4qIjgOCDLKZevo+8Pn4XfD3uQ1pS3LB9Tey2\nnt3wOiotnuSo/n8urGYLbndwVyQsFmvYXq+gwrTD4XjLMIxS4B/APx0Ox5peHvdN4NeGYfwPHT3U\nscAbwPXA74FPnPu3TzVDbLyiDCyr10NCbAR1jcF9YE9Ij6WiYniPURyqpo5J4MDJ4HoYRidHU1zp\n+8aUi1nMYDYHH9THpsUMqfdIRnI0Z3o57jZjlJXAoyg7jIpOpby581hik72lo3faXw+gta1jXGxQ\nvJgSKnxuNWHidHkxfz3yXcqaKzBjYnz8WLJi/Y8itI7Kp602nWAvko5N6/nnRYzVREykNegrBuNH\nDa33W09cMnMaW7YdJNjn2xTRgre98wlRakIklXVdr4Z0x+uMIcYaQ5Or82eDpy0Sc0xwz7G3zU7O\n6InD9jUZHzuO07UFQbVNjUrB1WCmorH/n4tJCRM4Wh3cMKtJ8eN79Xr5C+J++xkMw/itYRi/MQzj\nN3T0SL8HfOSix0LicDiKgVeA7XQE9LuA7wCfMwxjA5AEPB/q/kU+yGoxs2puZlBtzSYTa3OzwlyR\nhGpND16ba5ZPCHpKsfnGKBbPSA+qbZTdwtJZwbUdLG7/WO+mr7LbLFyzaHbQ7T80/lISIjr/8TGZ\nwdPif1EV6xgHwfZgmyKbsPi5QGkxWXj9zNsUNZbg8rho87RzovYU7xZuwWLy/efPklgNpuAvZ93+\nseCfl/MibBaWzwnu1iCrxcyq7OA+v4aiiWmjsEYHF4TBi9fZ9crClYvGkZ7UdUGf7kwbk8zKrMVd\nHjfZgh96Y3eOJtYe3PGGopVZS3vQdsmALTC0qgd1rh6zLGx1BOqZfjtcB3Y4HE8DT3/g4SvCdTyR\nyxeOZceRsoBjD69cPHZYrnY3XMydnMLsSckcOlXtt92sCUnMN0bhbHPz69eP+W0bE2nl2pWTsJhN\n5J2sDDgG9rpVk4fcPNPpydGMGxXbZbGWYH185UTmjh5HdsXsbm/eutjkhIksSM/BYjLz3JEX8V4U\njs1RDXhaIzHbuw9PtlHFuAoMcAfqnfZim+p/Rg6X13evr9vrPyxbxx3FdXYWgXpLp41NICE2tHnp\nr1o8jt2OcqrrW/22u3r5hHNDIYavT185mef+XESg59sUU4e3qfOY9vHpcayYm0FqQiQ/ffWg35uO\nI6xmPrl2MumpBrvL9lPpfP9zxBLbgKsmFWuS/9lZPK1R3LHk44F/qCFsdMwoVmUtZWPRNr/tMmLS\nWZHZ9cSkv8xOncGM5GkBe6dnJE9jVsr0sNXht2fa4XA8f/4/YA9Qd+7rv577v8iQERtl4ys35TJ2\nVGy3202mjj9u16+e3M+VSU+YTSbu+vgccqf6Xto3Z0oqd103B7PZxMrsTD59xTSslu7/SCfF2Xnw\nU7mMTo4mLTGKr9yUS6qPZbQtZhM3XjKFS+f3bvL/gfLwrYvITOm6VLY/FrOJT66ZzBWLOuZ0/veZ\nNzF/VLbP9jOSp3Fn9i1YzBYWjM7l5hmf7LSkr8nmArx4Wrqvw+uFKTk1xEb5PlkxAR+5PJaomO7D\ncoQ5+OBpMXXftZ06to41C/3f3DgxI46v3zw/6GN9UEKsna/clEuGj9fEbDJxzYqJXL1sQsjHGCpW\nGdP52OVJ0M2NgR28mKJruwTpqWMSeODGbOw2C9lTUrn9mlk+V4yMjbJx3/VzmZyZQGxEDPfm3k5m\nTOfZY8wJlbhqUn32UHtaYrh56qcwRg/fKwXnfXLaNazKWobJxwnO+Lix3JPzRSKtA9f5ZDaZ+cKc\nzzI31feVt7mps/jCnM9i9nM1qreCWgHRMIz7gZsAu8PhyDYM41GgxuFwfD9slQWgFRAlVF6vl4On\nqtl0oJjKWidWi4lp4xJZm5NFauLwvWw3HJ0prefdfUUUlDfi9cKYUbGszc1iYkZ8l7b1zW1syism\n72QVbW1uEmLtLJ2dzgJjFFZL5w9Zt8fD3uOVbDlYQm1jKxFWC7MnJbM6O7NXq90NFkdOV/PcG8eo\nqu+4rG0xm5g5IYklM9LZeaycmoZWbDYzsyemsDonk8RufubChmI2FW2joKEYL96OHqqsJUxKGN+l\nbWN7E9uKd3Gw8iit7lbiImKZn5ZNeZWbbcW7afHWY8JChn0sn5i9limjOoY/bD5Ywsvr3+uYlcUL\nVquZJTPT+eyV07BYLDS3N7O9ZDd5lYdxulqJtcUwPz2bf55+m5rW2qCei1kp05maOImDlR2L0sRF\nxLJo9DxyR83FZrbS2NLGk385jKOgFrfHi8kEo5KiuO0jM5iS1fsliqFj8Ze8k5VsPlBCVb0Tm9XM\njPHJrMnJHHFXyRqdTn7yzw2cOu3F67KAyUtMQisfXTKZynIrp4rr8Xi8jE6JZk1OJsa4pC77aHa6\n2HKohL2OClpaXcRE2Vg8M53FM9O7BG2P18ORKgfbSnZR1VKN1WxlatJkTE2JvH1qJ+3WejB5MLns\nTImbzr2rPorVOrSuSvVWeXMFm4q2c6ruLG6vm7SoFJZnLmZ6sv953vvb2foCNhdtp7CxY9rSMbEZ\nrMxa2uuVD8/r9XLihmHsBJYA7zgcjrWGYZjpmI1jSZ9UGAKFaRER+aDm9ma+sunhoNunRibzyLKv\nh68gERkW+mI58YZz09gBcO7rHk42JiIiIiIyvAR7reKkYRjfAZIMw7gOuBE4Er6yRHqupKqJ9XuL\nOFlcj8frJSMlmtXZ3V8GFBnJ6hpb2ZhXzMHT1bS1u0mOi2TZ7NHkTkvF0vvJpPtMRXMVm4q3cbL2\nDB6vm7SoVJZlLsJImuJz9oAoaxRJ9sSgh3kEmiZPRCSQYId52ID7gLVAK7AZ+Nm5BVYGhIZ5yHku\nt4ff/svhc3nnaWMSuPsTc4OeIk1kOHtzZz4vv3sSdzeL2aQmRHLf9XPJSuv+Jt3+4vF6ePXEa2wo\n3NppJpDzxseN5fa5nyPB3nVsPMAbZ97htVP/CupYd2XfxswUo1f1isjw1+thHg6Ho52OJb+/43A4\nrgN+PJBBWuRiz71+zGeQBjheWMejL+2nrT24VZJEhqt39hTy4rr3ug3SAJV1Tv73hX1UDvDS1a+c\neI13C7d0G6QBzjYU8Pj+p2lxdT+93orMJSTaEwIeZ3LCRGYkT+tVrSIiQYVpwzBuomOBlefOPfRT\nwzBuC1dRIsE6XVLP1kOB12Q7W9rAlg+0q2ts5VRxPfllDUGveicyVDnbXLy64WTAdvXN7fxt65ke\n7dvt8VBQ3sjJ4jpqGgL3s1TUtnCyuI6iikY+eHW0rLmCjYVbA+6jtKmMvzs2cKq4nrrGzseMjYjh\nnpzPk2T3PdvGxPjx3D73c12Gi5Sfr62yqUttPeX1eimqbOJkUV3A+e0BahpaOVlcR35ZAy73yP5M\nqnHWcroun6LGEjzdzAne7GzndEk9Z0rraWnt2dLs5zW1N3O2voD8hkKcrq7v23aXh/yyho73WFMP\nljvvAY/XQ1FjCafr8qlxBjc0SQafYMdMPwBk07FaIcCDwLvAr8JQk0jQ1u8rCr7t3iLW5mbhyK/h\njR35HDhVdWEu0bhoGyvmZnDlonHED/PFEWRk2na4DGdbcFdndh4p41OXTCE60v/QqGanizd35bMx\nr5jaxo6wYQJmTkjiykXjmD0ppVP7HUfKeHt3ASeL6y88NiopijU5WVw6fww2q5nNRdt99kh/0Lqz\nW3n9NSsmk4m5k1L40OJxF+6RGB2TzrcXP8CO0r1sLtpOeXMFJpOZCfFjWZm1lJy02VjM70+Ttv1w\nKW/vKeTURbWlJ0WxNjeLS+aP6TJ9oj8ut4d1ewpZv6+Ispr3Q/SkzHgunT+GpbM6z218+HQ1/9qZ\nz+HT1Rd+8oTYCFbNzeSKRWOJCfA6DCeHKo+yrmATx2tOXngfJNoTWJ65iLVjV1Jd6+aN7WfZeaz8\nQieI3WZh8cx0rloyjvSkwPOoFzYU81b+u+wvP4jL2/E7EWmxs2j0PC4btxqbJ5Z/7cxn04GSjmkZ\n6Zjze+7kjvfYtLG9nxLR6XKyvmAzm4t3UNtad+HxaUlTuHTsSmanzuj1MaT/BDtm+m2Hw3GZYRjr\nHSzLmykAACAASURBVA7H2nOPbXI4HCvDXqEPGjMtAN/45bZOf6wCuemyKbz4zns+J+RPS4zkqzfN\nI8XHoh0iQ9VTfzvM9iNlQbd/8FM5zJzge9GS+qY2/veFfRRVNvls88m1k7lqcce8079/8zjv7C30\n2dYYm8iXb8jmx/ufIL8h+JNk5/7VeNs65oc3meAzVxqsyQl+yXmA3/7L4ffEfPq4RL78yWwifCwG\ncrG2djc/eeUAR8/W+GyzJjeLz17ZMU77zZ35vLTuPZ+nDxkp0Xz1ptxhMb95IG+cWcdrp97wuT0l\nIo2K3Tm0Ort/HaLtVu6/Ifv/s/fe4XWUZ97/Z2ZOVe+9S9axXGTLFXcbMIbQHANJCAlkCSmk7JLN\nZpPsu9nNu+/+Nskmm02jJYEAgZCEEDqhGBv3blmWLPvIVu9dR/XUmd8fx5Z0rFPmyJLBZj7XxYV9\n5pmZx3Nm5nyf+7mf701hZuAUn6qe0/y26ve4ZP/RbLMnAeXsGvoH/VdBFQS478a5l1Tefdg1wi/L\nf0PLcFvANrfkb+Gm/OumfQ6NmWcmrPF6LBbLfYDZYrEssVgsPwK6Z6R3GhqXQKDcz0A8vz2wkAbo\nHrDzixdPXvL0robGhw13mM9KqGfr0ZerggppgBd21nKytoedx1uCCmkAa/MAz75tDVnmewrCRD8V\nxSuMa5rVT5dvP9occobrTNMAz74bvFzxBZ57tyaokAZ4v7yV7UebqarvDSqkAdp7R3n45eDl268G\nKrqrggppgF5nN0ru8YDbRx1ufv6Xk+PR5IvpGevliapnAwppRYH+6pKAQvpCm2fesnKu1RawTSie\nOvV8UCEN8Hr925zoqpz2OTQuL2rTPL4M/CcQDfwWr5vHA7PVKQ0NtSTHmemx+V+EdDGSKKgS381d\nw5xq6GNBfmLIthoaM43LLXP0TJe34p5HJinOzNqF6Zc8W5ISZnVPp8vDH987y/CYC7NBR1lx0nik\nur59EKtKwfq3g430D6nLNz1Y3cnizDhaCbygeDKKR0Jx+UZsFQXePdI8PhVvG3Gyr7Kd9t4RREGg\nICOGa+alYTRIyIrCO0eaVZ3rQFUHd2woJDYycBqYbcTJgVOh13AAvHOkmZQ4k6qElnMtNs612igK\nEnG90tnetFtVOym2F8E8iDLm38lleMzFvsp2tqzImbJtV8t+nHJgoSzbklDGokP2QVYU3j3SPK3v\no3W4ndN96gZm25t2szhlYdjn0Lj8qBXT9wOPWq3Wr81mZzQ0wmXdovSQUaALyGFE5vaebNfEtMZl\n50BVB3/ccZahUd8f/Nf2NbCiJIX7bpyL0RA61cAf60rT+dvBRlXizWSQePgl32joe8dbSE+M4IFb\n5gV1z7kYa7P6CJ5HVogYLQBOq2vfmw7y1Otx4lwPA8MOXt/fwO6KNtyeiX/1npPt/HlnLbevzScr\nOVL1YNwjKxyo6uDGlVNF2gUOVHX4nCsYPTa76nMD7D3ZdtWK6c7RbupsDarb65JbcTX5F9PgfX9f\nLKYVReFQ+7Ggx3V3qy87fbymm1G7K+S6gos50H5Eddv6wUY6RrpIi0wJ6xwalx+1aR564H8sFovV\nYrE8abFYPmmxWAIn02loXCaWWVJIiQ8dcTPoRJVLmrz0hvEjp6ExE+yuaOM3r1dPEdLgjYQdrO7k\nf1+omLbzTGpCBEssyaraBlqo2N47yn//oZyWrqFp9UEN0nCqqkIqiizi7sz1u80jK/zmtWp2HG/1\nK27HHG7++N7ZkKknFxPqvdA7OHvvjav5ndRnVxcQuYBgDL5OpsfP92D3OBhxjwbdT3Gqn/3xyIoq\n55qL6QvTsSPca6PxwaDWZ/pHVqv1RmA+8AxwD6BuLktDYxbRSSLf+MQiEmMCL84x6iXuuSE8L9lw\nVu5raFwqI3YXf1CRk1vTPMD7J9QvzruY+z9WQlHWpUU3HS4PrT3BRcmloJckHiz9O1LMSQHbKLKI\n89yioFPyamasymt6wuubLvh7IdT2S+FqfifpxfCiu4oc4ns4f61kRcHh9KAoCjox9ES8IIY3UJ3O\n961X0Y/JqOm3xgePqm/JYrGsAzYAa4AI4Cje3GkNjQ+c1PgIvnffcrYfa2FPRdu4H6jRILFqXiqb\nl2eTGh/Bq3vr6R1UF0koydVKkGtcPvaebMepMuL8fnkrm5dlT+s8ZqOOb31qMbtOtLGzvJX2Xq8o\nFgWB+fnxnKrvQ002VDi+vmajhMMlq06zKsmNJ94Ux7eWfY1dLQfY13ZovDS44hHx9KXj7sgLKqR1\nkqA63UIA1bNWc0O8F+bmxPPWoSZVxxIF7/cxYld3La/md1J2dCZmnSlgEZ6LkQeDT4xnJkXyyMtV\nlNd045EVDDqR5XNTyEjMom0s8GyEGN2HPKRu0j0p1kRSmOsQAIrjCjnaeUJVW5NkIidafeqJxgeH\n2iHPe8B24KfADqvVqpWS0/hQERNpYNv6Am5bk0ffkANFVoiLNmKcZGW1fnEmL+2uC3ksSRRYv3j6\ntkcaGuFysrZXddv23lG6+kdJUeGn6w+9TuL6ZdlcvyybXpsdp9tDTKSBinM9VNb1qT6O2gW96xdl\n0GOzc8wa2gAqMcZEaZF3rUKEPoKb8q9jS94m+uwDDAyP8aOnq/G4Q+eMqxXSAAa9hENFddTkOBML\nC4ILrYUFCSTHmegeCC0KFxUlkZYYwd8OhhbfBr3I2tLQqS9XKkbJwIq0pexq2ReyreLW4ekN/n6+\neHGs0y2zr6oDKTEBQ2FgMa1LacbdXgBK6IjzxrJMRCGgU1pAlqeV8VLtG6oGDivSlmDSXf2WiFcD\naucokoBHgBuBnRaL5U2LxfKd2euWhsb00EkiKXFmUhMifIQ0wPVLs8hMjgx5jNvW5hP3EfB01fjw\noEbM+bafmep4ibEm0hMjiTTpwz6mmsIVyXEmbromlzs3FBJlDj6VLwoC92wuniJQREEkyZxAUXIm\nt6+eE/Kc6YnhDTLMBpFIU/C40oW+XVwt8WIEQeCezZaQIivSpOPOjV4PbjVrPu7YUBj2QrcrjS25\n15JgCh19dzVbQJ5e6oOnNw2PLfDCcsHgQJcRukpoVnIUm8rC8zK/gEEysK3o1pDt4o1x3Jh37bTO\noXH5UZszPWi1Wl/Hm9rxHN5Zsa/MZsc0NGYa7xR3GcUBckZ1ksgdGwq4dXXe5e2YxkeeYHZrFyPg\nnYn5IPsAsKAggc3LspFE/8IxNzWaf757CTERBlITvIVHAtnzRRh1PLh1PovnBM6TBrhldR53bCgI\nmD88JyuWb35yMYYwclkTYs186+4ykgJYD0aadHzl4wsoLQzetwuUFiby1Y8vCCjQk2JNfOvuMtIT\nI4ky6/nnu8vIT/efsmLQidx93Zxpp/VcScQao3mo7EtkRfmPOhtEPXcVbWV58jK/2wXBm94THBHn\n2TJ0w/7PIQoi169MY+u6/IDHKs6O45/uXozZOP1c5tUZy/n03DswSP6fucyodB5a8mVijYEdSzQ+\nXKitgPgiUIbXr+gdYLvVaj01y30LilYBUeNSONdqY09FGz02O5IkYMmOY92iDK2UuMYHwtEzXTyi\nsjDHgvwE/vGTi2e8Dy63zDcf3hew4MVkRMHrW91xUfVRvU4kNzWabesL/OYXy4rCyXO9HKzuYHDE\nicmgY/GcJFbOS50ykxSMwVEne0+2c6apH49HISnWxLpFGePWcU+8Uc2+SnVr5O/ZXMx1S7OQZYWK\n2h4OVXeO963sfN/UVD68GKfLw6HqTsrP9mB3uomOMHDN/FQWFSYh+hmAWJv62VvZTq/Njl4nMS8v\nnjUL00NG9K9GrH3nONhxlAG7Db2kpyShmGvSl2LWeQdj7b0j7DrRRkv3MAKQkxpNTKSBP+04p/oc\n99+RRZtymo6RTkRBJDc6izWZK8ej44MjTvacbDvv966QHOe9xwozZs6ecMw9xsH2Y5zpq8HpcRFn\nimVl2lLmJoSegdG4/ASrgKhWTG8E9lutVqfFYsm1Wq2NM9i/aaGJaQ0NjasFjyzz3ccPqvIdfuiu\nRZQWzo4H+l931/H6/oZLPs4dGwq4eVXeJR9nujR2DPEfTx8JWu0UvFHn/35w9SVFGTU+HLy4q5Y3\nDqiXJhcGURoaarnkcuJWq/V9q9V6oYTV72akVxoaGhoaAEiiyNfvKA0Zhbx9bf6sCWmA29bkzcjx\nX9xVR3374Az0aHrkpkXzmRssBJv0N+olvrZtoSakrxLCXQsYIDtJQ2NaTMe4UrsFNTQ0NGaY7JQo\n/vXepVwzP3VKvmZOShRfum0+t6/Nn9U+6CSRr9+xkDs2FBAf7bsIN9KkIz9dfQ7njjALosw0m8oy\n+Ye7Sqf4aouCQNmcJL77mSVYcq5eu7mPGnlp4eUX54bZXkMjGKrSPCZjsVjut1qtT85Sf1SjpXlo\naGhcrQyOOqlrHcTtkUmOM5ObFthTebaQZYWa5gGGxlyYDRLF2XF874lDqmzfwGvn9tg3N85uJ1XS\n2j1MR98ooiCQmxZNQoz6SncaVwayrPCtR/erqkqYmxbNv39u+WXolcbVRLA0D7VFW35mtVofAvgw\nCGkNDQ2Nq5mYCENIZ4vZRhSFKYsIR8bUF2txumRcbg96XfiL92aazOQoMpOjPuhuaMwioiiwbX0B\nT7xxOng7wdtOQ2MmUZss5rFYLNcC+4ELudNYrdaZMTvV0NDQuAy43DL17YM4XB5iIw3kpF7+iO+H\nhfqOXp7eWY7d6SIzIY6v37oq5D4RJh2jKqsf6iRRlZD+zdtHqWvrR9JJ3LWmhEUFvsVJdpyp5M3T\nB5AVmfy4LL5+7S2qzj/T/Psf/0b/2DAGnZ4Hr19LYbrvYMfmGKR9pBMFhfTIVOKMsbg93vttzOEh\nJlIfMhWhrWeE3kE7ekkkPz0GoyHw9Rt2jtAy3IaiKKREJJNovrpSVh7Z8TYtA70YdXruW7WBvOSU\n8W3+rjXAmoXpjDrc/PG9s34Xn+okgc/fPI+FBYn0Ddq9FUAFyEqKJNZPbYHj9U28cqwcl8dDcVo6\nn9vg+4x4PB7eqD5K79Aw0WYjN89fjtlgpN8+QMdoFwICGVFpxBgCv2ecHheNg004ZTdxxhgyo4IX\n52keamXQOYxRMpAbk41e1NE63M6pXisKMpa4IvJic3z7KXtoGGzG7rETrY8iJ8a78LJztJvesT50\nonS+CmVgz/NR1yjNQ23IikySOZHkiMBrK2RFpnGwmVH3GBG6CHJjshCF6WQVe2kZasPmHMIg6smL\nzQlakn3AYaN9pBPA596YbdS6ediAKCYqrwqAYrVaP7CQg5bmoaGhoZZRu5s3Djawp6Ldx/otKzmS\n65dls37RR6fi5f7TTfxux2E8o9HgmVjwKJiGiYxU+MUXbg+47592nOXtw82qznPN/FS+eOv8gNv/\n6bdv09cnXFSAQ0HQO7l9dR6tzgZOjhxEMI75LC5T3DpMw9n8dNuDqvpxqXzpd08jJjciRgxP9MFl\nwN2dyWeXbiE3y8DbDTs42VONrHjjSyIiiUIO/bU5DPVMFJFJT4zg2iVZXLsk06cAzNEzXbx1uIm6\ntolFm2ajxOr56dyyOtdH6HWMdPFWw3uUd1filr0DGwGBkoRituRdS1Hc7ObVzzYP/flJHOZWxMih\n8c8Utw65P5Wb5i6nS6zlZM+piWstiCxMLGFL3rXkxnj9uLv6R3n/RBvHrd2MOtxEmfWsKElhw+JM\n+occvL6/gZO1vcjn9Y8kCpQVJ3PLqlxyUqP5y8HjvNuwFymxHUGaKKjkGYwn1j6H/3fn7fzgjddo\nqtOjOCYKgQl6O+a0DuTUGgTR2z9JkFiUPJ8b867zEcrDrhHebtjBwfajjLonLCazozO5NnsdK9KW\n+FyXfW2HeL95H20jE5aPRsl7Xzg8vqkt0YYobiu4kWWpi3mn8X32tx3C5py4nnHGWHSCjh77ROVV\ng2Rgeepibsq7nnjTREGmnrFe/tbwHsc6K3DJE+/O4rhCbsjdREli8fhnbtnNjqY97Gk7SJ+9f/zz\nRFM86zJXcW32OiRRvWw80HaEnS17aR1uH/8sSh/JqvTl3JC7iQj9hPhvGmzhrcYdVE5+DgWR0qR5\nbMm7dkbKsl+yNd6HEU1Ma2hoqGFo1Ml/P19Oa/dIwDYbFmdw341zL2OvPhjeOlLDC3tqUJyBqwRK\n0X385qt3+t3W1T/Kd399MKTlHMC/fHbpuO/zxXzx52/gHgscBRNTajHkng3o0KAooAwm8+jHvxW6\nI5fAl/74c3QprQG3y3YTOpMLGf8VLBVZxHl2MbItxefzlfNS+cKt8xAFgZf31PHqvoaA50iMMfHt\ne8pIijVTb2vk4YonApaiFgWRz5Z8YooQu1L48p9+gZQceOGqbI9AcRiQYgembNOJOh5Y8BkWJs0L\nuH95TTePvlIVsNy8QS9SOMdDvWEngsHpt42igPNsGfJAasDziFH9GCxHfYS4QTLwYOnnKI4vwuYY\n5GfHH6NrrCfgMa7NXscdc7yVEp87/QL7248EbBuIGEM0g5NEtNp9/qHsS6RFptAy1MYvT/yGYZf/\nd6eAwCeKt7I+axUu2c2jFU9i7Q/s9T0vwcKXSu9DFySyfIHnrX9lb+vBgNvTI1N5qOzLRBkiOdV7\nht9UPoNL9j9rphd1fGHhvcxPvLR3/CVb41kslniLxfJji8Xy+/N/v9VisSRfUq80NDQ0LgNPvHE6\nqJAG2HWijZ3lgUXT1cILB6qDCmkAz1ACX//Ny363pcRH8Onri/1um8zNq3IDCul/eWp7UCGNaA8q\npMFrgybEdPPdV58K2Zfp8sXf/S6okAYQTfaAQhpAEGUMRRWg9xW/h6o7eetQE+Vnu4MKaYDeQTsP\n/7UKu9vBYyefCiikwTu9/uzpF8anua8k/vHPvwsqpAFE0yiI/oWwW3bzZNVz9NunCm2AHtsYj796\nKqCQBnC63NTp9gQU0gDutsKgQhpAHo7H1Vjie2yPk19XPsOwa4Qnqp4LKqQBdjTv4XDHcXa17J+W\nkAbCFtIX9nn85FM4PU4eO/lUQCENoKDw55qXqbc18fK5N4IKaYDqPiuv1r4Vsg/7Wg8FFdIA7SOd\nPF39RwYcNn5b9WxAIQ3gkt38tupZBhy2kOeeLmqTWH4LNAMXsvaNwNOz0iMNDQ2NGaK9d4STtb2h\nGwLvHlGXvnCl8tOX96CMqrMDGx0J/NNw3dIsvnDrPL/lt2OjDNyzuZg7NhQG3L+jO7D4BNDnnVHl\nGSwIMKCvDd1wGtjtdqTUmalNJkgedClT7633jrXw9qEmVcdo7BzitdN7gwqbC3gUD7ta9ofdzw+a\nMbO6wawUbcPV6T8tyym72Nt2yO+2neWtON3Bl3mJCe1ewR4ARRZwd+YE3D4ZT28GisvXN37MbefN\n+u3U2upVHWNH0252Nu9R1XYm6Rrr4bW6t+l3+B+YTEZB4b2mXaoF/762wzg8gQcr4B1IqKG6z8rb\n9TtwhjgeeAcz+9oOqzrudFArppOtVusvOL/40Gq1/gUIHt7Q0NDQ+IDZX6WupDRAR98ota2zF7n4\noDndqj5aqdij+MVrgQXZqvlp/PDLq3jorlK2rstn69p8vrJ1AT/5yuqgVeXeOlIDnuBTvFKsusEP\ngGBwsO9ccPeG6fC9F94KKqrCRUqaKhT7hxzUtKi/3w53HlPftuPYeN7olcDju95FjFRf5EcIEJ0G\nONh+1O/nasrL6xKCPyOyLRncUxcq+kUR8fROFf1HOo6r2x9oHm6je0z98zCTHOkoV922ortKlaAF\nsHvsVHRXBdxeb2uiY7RL9bkPd6q/nocC3BszgerSTxaLRY938SEWiyUViAy+h4aGhsYHixrP2Utp\nfyUhy+HV22rtCx6VEgWB0sIkSgvVW/idau4mZN0vKXjkejKCAMeballTVBK6cRgMu0emVdEsEIJB\nnTd3MBxK6Kj0eFuPE7vbToT+yoh5Nff3Qhg1VAS9K+A2m2OqKJdlhcGR0GIv1PekOMPzJ1dcU4W3\n3XNlvGPsHvX3rEx4S9iCpVvYwkzFCOd6Dvi5N2YKte+LXwJHgPkWi+VVoAL4yaz1SkPjMuD2yPQP\nORgaVTei1rjyMOjDMxwy6GdSQl0aQ6NO+occuD0zE2EMt9yyXhe8tDl4c3QHnUPYHEOqIqFRZkPo\nEyvhdTTGGDqu45E92ByDDDmHUbPoXlJm2KhKvvTjiepjXwDoxdDf33RwelwMOGyMusZCN1aJWa/i\nvpiEIgd+Ti/Ypo053PQN2rE73YiiMKWqqP/jhvieRPUDPQCEqc+EdAkWcZeTS7GyC0Wwe1MvhXcv\nSIL6Z2u2nglQGZm2Wq0vWCyWA8AqwAF8yWq1tofYTUPjQ0l77wjbj7Zw4FQHdqf35ZiRFMmmskzW\nlqZjDFOAaXx4mZ+XwPsqFxYa9RJzsuJCN5xFHC4Pe0+2s7O8lbYebyTSZJBYNT+N65dlkZ44/QnB\nCKOO4eHQ7QCQXLQ1izzyUiXXL8umONv3ugy7RtjTcpB9bYfG8yqj9VGsyljOhqzVU7xdGzuGePdo\nM4eqBvFOcAb+oZbHIpGi1EWQFFlk1x6FrqZyrl2SyVKLr2tG71g/u1r2cbD9KCNub9pGoimBtZkr\nWZtxjY+1lsvtYW9lBzuPtzLaF4UpW/JxY7gUZNvU6L0kCkSYdAyNBo6yTibbnE+d86SqtkVx+eil\nmRUOdbZG3m/eS0V3FW7Fe10KYnNZl7mKZamLL0l83XvNen5w9FjQiPNkFEfgBawZxjx++ucTnKrr\n8/r4CrCwIJHslGjq24PfV/JgAlJMf8DtYkwvIKM2DinGTl1kmBOdRa2tQdX+EZIZt+LGKau7LjNJ\nTnQWZwfqVLVNNSfTOdat+tjzEgIvYi6IzcUoGULmVV8gJzqT+kF1aw8m2/jNNGrdPKKArcAmYAtw\nm8ViCbIcW0Pjw0n52W6+/7sj7CxvHRfS4C2W8Ny7NfzwueM+PsQaVzZlc5KIj1aX43jN/FTMxvCi\nfzPJ8JiLHz53nOferRkX0gB2p4ed5a18/3dHKD+r/gfrYv7lzk2gU/cDJUYMAhJHrd388LnjvHGg\nYXxb12g3PzryC16v912gNOQa5p3Gnfzg8M9oHJxYcLenoo3/9/TR8/nrAoIheETT2ZqnynoPwGNL\nAOB0Yz8Pv1TFE29Uj3sHnxuo5wdH/pf3mnePC2mAXnsfr9T+jR8f/SW9Y17hNGp389/Pl/P7t620\ndA+DJwJPX5q6TqjA36K1pZZkNpVlqtrfZJDYNn+T6vOtz1ytuq0adjTv4afHHuFYV8W4kAavwH66\n+o/8uvKZcc/r6ZCdmBTSIeMCikuPmBh4gGwtj6PqvJAGr5XdydrekEIawN2VhhIkHUo02hHj1D2D\nQoQNKXpqqtTthR8jUmX6zaqM5SxNXayq7UyiEyTunHOr6gHStTnrKIzNU9W2OL6I1MiUgNvNOhPL\nVVo7xhljuaVgi6q2ABsyQxemmi5qh5J/BFbgTe+oBNYBz89WpzQ0ZoOmziEeffkUriAruhs7hnjk\npcrL2CuN2UQUBe7dYkEMkeOQGGPk9rUfbLGLh/9aSWNHYCsrl1vmsVdO0dylNrzsS1p8DIJxBELk\nNwqGMeRR34ptL+6q42B1B06Pi4dPPOFTkOFihl0jPFLxJEPOYU439PHUW2fGBS6AghOEIBFfWwby\nUOhqfopbwlW7wOezfZUdvLKnnn77QEgbua6xHh45+SQe2cPjr56ittVXbLlacpAdwXNk1Yh+d08G\n8pBvtbiYSAN3bihky4ocspJDzzZ8+vpi8uMz2ZyzMWTbBYkllKUsDN0xlVR0V/Hi2ddQgtw3lT3V\nvHD21Us6z31Lb0QeCy4yFQVcPelIBv/C3d2TgTwYuDJfSNzReFqDRy/1OdbQg1LRgyGvesrH6zJX\nURiXx6cs2xBCrB1IiUjihrxN3FJwA/HGyztjtrXoZrKiM7k5/4aQbefEFXBN+jLuKt6KSQoeuDBJ\nJu48750djJvzN5NoCv4OEAWRT1k+ztyEOVyTtizkMa9JW8ac+MAuQ5eKWjEdb7Va77VarY9brdbH\nrFbrZ4BLuGM1NC4/bx9uVpV/eqZpgHNhrLLX+HCzqCiJr21bSEyk/1y8/PQYvnPPUuL8lBO+XJxt\nGcDaHNqGyuWWefuwuinNizlU3YEyEo8QORBQDAjmQW9kzjP1Wr15oJGjnSfosfeFPNewa4R9bYd5\n82DjFMGpz2pCV3QUpEAzQAquxrl4bPF+xaqigOI0YD+5AuSpYnf7sWbea9jPmDt0Tm/HSCfbrRVU\n1vlxTHDF4jy9GHnY/8o4xWlAbF7qrermL29TEXB35OCq8xW2WclRfOeeJSTFmTEbdXzr7jLm5/kX\nDhFGHZ+/uYS1pd7KeVuLPsbH8jf7LacsILAybSkPLPzsjOa7vt2wU1W7A21HpuVrfIFVRcXcnrMV\nz5B/4ai49AjdhZgzpkalRUFE7M2bcq39YdCJSOJUIauTBG5cmcOj994PrSUoHv/pfspYFNeuNSOa\n/Q9qBcMYBssRxKiJ3xCdILE5ZyOfLN4KwJKUUu5fcA9Rev8DqaK4fG9BEn0kccZYvrHky+RGZ4f8\nt00mLTKFr5TeH1Y5bZNk5BPFW9mUvRaAG/Ou5eNFN2Pwk8csIFCWUsqDi+5HJ+rIjs7g78u+SJLZ\nvzRMMSfx0JIvhSyXDt7CMd9Y8iD5Mf5tCKP1UXxhwWfHC/TcU3Inm7LW+n0OJUFiU/Za7inxX4hq\nplBbTvxZ4J+sVmvH+b+nAj+wWq33z2rvgqBVQNQIh1G7m2/8am/QqPRk1ixI4/O3BK6kpXHl4fbI\nHLN2c7K2B4dLJjbKwOr5aRQGKC5yOXni9Wr2qbTx0+tE/vdra4kwhZeS8s2H99I/dEFEywgXfuwV\nAUGUkV0GsEcFPUbe8no6Bauq88UoaXQdWewbz5RcmMp2jpdadjTMQR5I99rlCQqC3o6UV4E+73kE\nTwAAIABJREFU2puWoXTnohhHwDiEICjILiPu9izkvtyg544qqsGToC7fM7J9FT3Nwe8BMaoHKbET\nQe9AkXWII8n82y03kZ7sTTOxOYY40H6E1uE2FCAzMp2VaUupb3JSfrYHu9NDdISea+alYsnxL5yb\nu4bZe7KdvkE7ep1ISW48K+el+l1EO+Ia5UD7EZoGW5AVmdTIFFanryDRHDqiHw4tQ2384MjPVLe/\nvfAmbshVn44SiJ++8zo1A7XeQZ8sYvLE8c3rbyc7MYlB5xD72yZf6zQiRgt4+rUG1cf/3I1zGRh2\n0NIzggDkpEaxrjTDZ8Bd1dzKo7vexW3sQxAUFKeZZamlfOG6teNtnj30PofPdOJyikg6hQWFsWxb\nupQjXeW0j3QiANlRmazKWE60Yeqz5ZLdHO+s4HRfDS7ZRawxlmvSlpIT499ess7WyOGO4ww6hzBK\nBhYkziXVnMLLdW+OF+pJNify8aKbx0ure2QPlT3VnOypxu5xEKWPZHlqGdGGKPa3H6Z3rB+dKFEY\nm8+KtCWYdFODCmPuMQ61H6fO1oBHkUk2J7I6YwUpEVPXAsiKzOm+Gsq7Khl1jxGhM7MkpZSShGKE\ncFdB47XKO9xxHJtzEINoYEGihcUpC/1WUfQ+h4dpOV9+PCsqnVXpK4g1Rk9pOx0uuZy4xWLZAywB\nTgESYAGqATuA1WpdPyM9DQNNTGuEQ0v3MP/2hHrD9sLMGP7PZ0NPHWlozAT/3++PTkkzCMZ/fH4F\nWcnBhe/FfOkn76seTAYias4ZPPENqtp6bIk4rct9PhPMg5gWqi8oogzH88ht32XH8RaefadG9X66\n9Dr02erayzWrcAyoH1D9673LKMgIw8ftCuZ410meqHpWdftV6cv5TMlds9gj/7y2v4GXdqsbPAHc\nubGQj10TfECmoXExwcS02tDGv85QXzQ0PhD8TesFb39l2BdpXB1IYUZswr2fIaS7sypEkSDFsy86\nn98ThtcLQRHPnzfM3vuxJAvYNMxDT+faX6mEmy7yQdm+hf9+/+h8hxqXB7XWeLsu/NlisXzJarU+\nPntd0tCYeZLjzMRE6BlUaUNV+BGJPGl8OCjMjFVdDS8mQk9yXPhmSqnxETR3T2/xInhlcH5aDDUq\n7YVzUqNoPCf6lHBW7JHe3FeVFmhRJANQlBFeKk5iooDaOH9qikRj4PWUPkQYdaQlXhmFUGaC3Ogs\nREFUXU0xL/aDifaG+74uDPN+0tAIxXSGkZ+c8V5oaMwyOklkbenU0q7+EATYqNKySkNjJthQlqk6\nQrpuUQY6KfxX9xduvbQ1APPzE9hcvDx0w/NsylvJ8pKLLLAUEXd34HLjPk0V+ETpdQBkpURRlKVO\nACXFmrhhofqKiFtXzlcdqVy9MO0j5UMfb4pjYaK6axmpi2BpyqJZ7pF/LDnxZCap82DPCeNe0tBQ\ny3TEtDY/onFFsnl5NokxoR0brluSNa3In4bGdEmJM3PtktAiMzHGyOZl4a3qv0BWShRpCdO7rw06\nkY+vL6AkoZh5CZaQ7XOjs1mauphbV+cRedFCSXdnXkjLOYCIwSKW5k9EOu/cUBiyip0A3LWpiFUZ\ny8mIDO0TXZa8kEUZhdywIvQ1jY0ycNPKj16e7c0FN2BUUZXuloItGGa4UEw43LWpMKQFpiQK3Llp\n9uzRND66qFqAOBmLxZJhtVrbZqk/qtEWIGpMh87+UX72wkk6+0b9bt+0JJN7ri8OP0fzI47LLWNt\n6mfY7iLKpMeSE49ed3nyJ2VF4WzzAAPDTox6CUtO3AdafGW6yLLCc9tr2Hncf0GK1IQIHrqrlNT4\nS0sz+OdH99NjC+y/fDERJh1f3bqAkjyvc4XD4+TJqueo6j3tt31BbB5fKr1v3ParsWOIn/+lgoHh\nCTs+wTSCofgYosn/c2iyFfKD2z6PQdLRP+Sgrs2GR1awjTh4aXe9T8GlC+gkgc9usbDu/AyUzTHE\nYyefpGnI//UsSynlvpJPopf0KIrCn3ac450jzX7bJsaYWF6SgsstYzbo2FiWQUJM6AFBuNTZGuiz\nD6AXdRTG5hNlCBxt7R3ro2moFVmRSYtMUWU5Nl1qBxr4deXTDLtGpmwTEbm96Cauz9ngd9/W7mHa\neke9rhlp0aSoCFS4XC5+uedVukcG0Es6ti1cy+LsQnptdurbB5EVhbSECHJSfV0aDp/u5Mk3Tvuk\nFl3AoBd54OZ5LJvrv2BIbZuNXpsdSRQpyoolNtLAkZZT7Gjah1t2kxudzWcWhfZIBmgf6Rx318iO\nyiQ5IrCT8KhrlLMD9bhkF3HGWApj8wK6XsiKzLmBOgadwxglA0VxBZh1M38favhnJtw8bgISrVbr\nsxaL5Tm8BVy+bbVa/zpz3QwPTUxrTBePLHO8poc9FW102+zoJAFLdhyblmSpnirU8OJ0eXh1XwO7\nK9p8KkdGmfWsX5TBbWvy/Fp7zQSyovDO4WbeO9ZC7+CEODTqJVbOS2Xb+oKA3tIfZlq7h9lR3kpN\n8wBuj0JyrIl1izJYUpw0IwtjK+t6+OWLJ3H7WUko4i2WPJmEaCPXL8vmhhXZPpG/OlsDu1sO0DzU\nigKkR6ayNnMlc+Pn+IiBXSdaeftwEx19vsnWknkYzANIye0IxjGQReShBOTBBLatnk9pehEv767j\nxLkePPLE6z4rOZKc1Gg6+0YZGnNhNupYUpzM+kUZxE62Nqvr5ZX99dQP1aJLaUYwjYAiECumcKtl\nA2sKp6YvtPeOsPN4K2ea+nG5ZRJiTAyPOWnpGplSsiQjKZKvbF1Axgy8M/a1HuK95j10jnaNf6YT\ndZQll3JrwQ0kmhPGP28abOH1+neo7rX6FFLJj8lhS9614967M82oy85TB96jerASj2gHj4RnIBm9\nLY/VxQVsXZ9PpGkiMl1Z18tr+xo41zqxFkDAmy50+7r8gHnL//jyI4wZOxCNE8+04hGRh+NxtuXC\n0IQYzk+P5pbVeZTNSR7/rHtgjN++Xk1tq41Jtw3REXo2lWVyy+o8nzSpfZXtvHWoidZJVUelpCYM\neWdQBNk3/UqBVEM2/7bu6377frqvhr/Vb/cpFy4gYIkv4uaCzRRMqhI44LDxWt3bHOuswDWpXHiy\nOZGN2WvZmLVm/DNZkXmvaTe7Wvb7VB01SgaWpy3hlvwb/FrvacwsMyGmDwC3AsuB+4AHgNetVuvG\nGepj2GhiWkPjg8Xh8vA/fzzh82N5MUWZsXzzU4tnPM9UVhR+/eopDp/uCtgmOc7Ed+5Zqrqc+EeB\no2c6eeTlU9Pad0VJCl+8bX7IqfTJ/OHdGrYfa5nyuRjXGbx8tOhCJ+pxB6lQfceGAm5eled3256K\nNp5660zACoU6SeRr2xZSWhg4YuiWZb796AH6hxwB20iiwL/dt4zs1On72P6l5lV2tuwNuD3aEMU3\nyr5MamQKZ/rO8tjJp3zE12QEBD5p2cq6GS6b7JFlfvViJRW1forbnCcjKZJvf7qM6AhDyOuv14l8\n9eNTr/+DL/0YMTZwuW7FacBRPw/FNpHCIwB3Xz+H65dlny8Lf5ymzsALbUty43norkXodSIv7qrl\njQONPtultFr02WeDrmEwEsVPr/03n88OtR/j2TMvBFysqRMkHljoLTTSM9bHz44/5iOML2Z1+nLu\nKbkLWZF58tQfKO86GbBtsjmRbyz5yoz5KWv4J5iYVhvmGLVarT3AzcDvrVbrMOodkjQ0NK5C/vTe\n2aBCGuBcq40/7Tg34+fefqQ5qJAG6B6w85vXpiccr1YefWX61+Pw6S7eDZAG4Y+D1R0BhHRHcCEN\nIAcX0uAtcX7ajw1Ha/cwz7xtDVrq2+2RefTlKgZHA5eF/tWLJ4MKaQCPrPDfz5cH72gQjnaeCCqk\nAYacw/y68hlGnKP8tur3AYU0gILCn6wv0xwgtWW6vL6/MaiQBmjrGeF3b56hpXuYp98Kfv1dbplH\nX6liaNL1/6dXHgsqpAEEgxND9lmfzxTg+e1nqWsb5Nl3rEGFNMDpxn5e2l1H+dnuKUIayRlSSAM4\nGObnB58e/3vnSFdQIQ3gVjw8WfUcNscQT1Q9G1RIA+xvP8K+tkO817Q7qJAG6B7r5ZnqPwbvtMas\nojax0GSxWL4F3Aj8k8VimQNoy2E1ND6ijNhd7FdZsW9/VTt3biggwjQzi5NkReG941NFmj/ONA3Q\n3DVMdkoUYw43+yrbOVnXi9PpISbKyOr5aZQWJYYVbb1S+cv754IKHDW8d6yFzcu96R6d/aPsKm+j\nocNrQpeRFMnGsszxYjLbj/r/jhTnzOV4bj/aTEmub9W/HcdbfdJCAuFwedhT0eY3uu2WZarqQpdN\nBxixuzle08WSYv+5uMF4vzm4kL5Ax2gXr9a9xZg7dK67gsKulv0zVjzF7ZHZWa5OnFfU9mA0SMgq\nbjSH08Oek+3jxVNGdB2qontixAhiah1yZ8H4Zwrwt0ONnDjbo6qfuyvaqO+Yap5oKKxQ7apTM1I9\n/uddrQdU2Qc6ZRev171N05C699fOpr3YPcEHdBc403+WtuEOMqJCL7zVmHnUiukvAl8A/s5qtdot\nFssW4Duz1y0NDY0PM8es3X4X+fjD6ZI5au1m/SJ11oShONdio3tA/QK6A6c6aO6K5Pfv1OC4aOHa\n0TNdpMab+dodpVd9vvyuE5e+brzHZsfa1M+h6i72VLT55BGfaRpgx/FWlhYnc9uaPOra/Dg9mwZR\nRuMuuR8XqDjXy6jd5TNQO3BK3SAP4OCpTr9iesfRFlTo8XHePNgUtpjuGu2hfrBJdfvjXRWq2x7t\nPMHdlm1I4qWnV1U39DE4EjiCPxlFgWPW4DNGkzlwqoOPXZPLkwfeQTT7X4zqD11MH85JYhqg/GwP\nssovbdThxto0NTIsxqgbQIF30FLRcZZFaXM40nFc9X7l3cGjzJNpH+1U3RbgSGc5t0fdFNY+GjOD\nWjF9q9VqfejCX6xW669mqT8aGhpXADaVP67TbR8MtT/sF6httfH2oaYpC8gu0Nk/xo//cJz/c++y\nq9oS0emamcy8l/fUczZIgZljNd0+C0J90M3cfQDeWYqhsQkx7XB5/Dp9BCLQfdkTqP8BmLz4Vi1D\nzvAK6Dg96s/hkl3YPQ4ixUsvMBPus+v2qB+F2M47vLQP9apXIwC6qTlAaoV0UAT1xxAEqO9rYkFK\nAaNulZWM8LrhzBaDjqFZO7ZGcNTmTC+wWCxFs9oTDQ2NK4ZwFxSaZnABotEQ3rEaOoYCCukLDI66\neHlP/fQ7dQUwUyWUgwnpCzR0BPhRV2beLnHA2cfYeTGj14lhpez4u49tI05c/qxOgjAdG0iDCu/m\nyYTj5CIghH38QIT7rIdzl5nOP8uRhjBTf+RZst1UwntG4s2xSKKETlB/jaQw2oaLUXfluRddLai9\nI0uB0xaLpcNisTRZLJZmi8Wifn5KQ0PjqmJRYaLqH00BKC0K7JoQLsVZcZiN6n+QXCrTUY6c6ZpW\nhPFKwZITH7pRCMIR5H4LrIzEIRjVT+eHRPDw86pf8J09/8GTVc/RPNTCwoKE0PudZ9Gk+7L8bDc/\nfr6cf/zlXnadbICQQ7AJsrLV3WOTyYxKI8Gk/jvJj1FfMMYSX4RenBmv9Xl5CWENFgoy1Zf2XlSY\nBMDnll2P4lIvBD2jU23g8tLUO1mIAn5noRR7GJF8BTbkLwNgfpL6ipsFYZRcj9JFqiqYc4EFKqtV\nasw8ap+QW4EiYCWwDlh7/v8aGhofQVITIpiXr060zMtPuORCI5MxGiRWL1BXoMIURhTb7ZE5pyLq\neqXyxdsv3X84HDHtf7pfRDDP4DVWJHAZcSsejnVV8JNjD5OZrz5FY1NZJgB/3nGOX75YyenGfhRA\njBlAMKtMwxBkorLbw+66KIiszVipqq1ZZ2bbnFsRVA5h12fNnDVelFnPigCFTi4mKzmSW1fnqz72\nxiXe6x8XFY0wom7ArbgMeFqmTpTfuDKHQpVCvmxOMtcvnVpx1FG7UPUi3Thpwtt6fRhWhFsLP0aM\nQZ3wX525ghVpS1W1TTYnUpJQrLofGjOLWjF9XYD/NDQ0PqLcs7mYKHNwh44os57PbJ75F/zta/NJ\nSwgu0HWSSFGG+igZeAX11YrZoGf1/BCWdEFITYgIWc77YvLTp4oGeSAZIWLmBLUyacpfVmR2Db3E\nIktosXLL6jwyk6PYdaKVtw77TrQKooyhqByEUPeDgi7nDDIhfPwCsCl7HXkxOUHbCAh8eu4dZEal\ncUvBDSGPuTRlEaVJ86fVn0DcsbGQxBAVHw16kc/dVEJpYSJrFoR2lLh1dZ7Pot+H1n4CeSz4ImBF\nEXB3ZoLiG61daklm2dwU7t0yN+SsVWyUgU9dN4eNZZkUZ1+0GHYsDnlYxQJZReA7K78y/te5CXNY\nnb485G435G4iJyaLz5TchSgEl1+ZUelsyd3ELfk3kGJOCtpWJ+r4TMknAlZO1Jh9pO9///shG/3q\nV7/6eyD3/H9zgLsB8etf//rLs9q7IIyOOr//QZ1bQ0PDK5QXz0mitm1wfCHRZHLTonnorlLSEmfe\nJcOgl1g+N4WW7mG6BqYu/kmJM/Pg1gV4FAVrc3A/18ncdE0OcVFXb5GXJZYUemxjNHeFt/gtOVXm\nnpszaWxxqV6QFhtp4Hv3LadrYIz23sllqEXwCIjRNhSHiSlZtoIH1XEewYM+6xyCOBFOVFDIzdYz\nP2E+TZ1DU2zy9DqRbevyuW1tPoqi8NirpxixXySGRQ/6jHqk+A48veneCPiUc8vocs+gT21iQeJc\neu39HGw/yqleKwMOG6kRKehCuGlIosSSlFJ6xnrpGJnqghFnjOXeeZ+gLGUhAEVxBUTozNQPNuGW\nffusE3VsyFrNpyzbEAPkV4+4RtnXdogjncc53VeD3eMgxZwUUtiZDDqWWZJp6hzyW4o+PTGCr21b\nOF7VcPGcJJxumcbOoSkLA81GHdvWF3DrmjyfzxMjY8iJzOFIQy0Y7FMs6mSnEU9HDu42y8S/WRLZ\ntCSTz900F0kUiY00sLAgkerG/qnfKd4iUg/dtYikODOSKLB8bgp9g3baekbHk3o8PVmIUX0IxrEp\nfVAUkNDxbyv+mYRIX3fgBUkleBTPeIl3n+snmbg5fzM3F2wGICUiidyYbGptDVPsDgUEFibN44sL\n7yNCb8YgGViSWkrbcAc9Y1O9vpPNiXxh4b0UxamfEdCYHpGRxv8baJuqCogXY7FYIoDfWa3WT15K\nxy4FrQKihsaHh9pWG0fOdDFqdxNh0rG8JCVgueCZpr13hP1VHdiGnRj1EqVFiSzIT0AQBLoHxvjO\n4wdUTd1mp0Txf+9fMfsd/hDg9Hh48rXTnGrowyMrRBh1bF2Xz5qF6RysaealY8foHx1C0DuQktoQ\nzV4xHDVQSneNOovDW1bnsm19IQA9A2Psq+qgd9COXicyLzeesjnJHKit4c/7TmK3gyAqZCSbuL5o\nCb99o0bVOaSkVgwFlVM/FyT+a/W/8sruFt4vb5siqGMi9Hx8fQFpCRH86A/+i64YF+xFjPAOOtz9\nSbjbCsFtANGDlNiOlFaPKHrFj1EyYvf4iiKzzsTmnI1sybtW1b+ld6yPgx3H6LcPoBd1WOKLWJg0\nz6+9ndPj5GjnCRoHm5EVhdTIZK5JW0aUwf/A1SN7eOncG+xtO4jrIhEea4jm9sKPsTJdXTpBc9cw\nB6s7GBpxYTJIlM1JoiTPf8rX8JiL/ZXttPWOIAgCuWnRrJqXFnAR8ZjbzvNnXuRwbR2Ky4QgyaAI\nKLJAQqKbJaYbGbOZ8cgKaYkRrFmYTkzERJS6s2+UJ9447beYVHZKFF/dtpAUP7nSfYN29lW2022z\no5dELDlxFGSbeKz8Wbqc7SgoGEUzdxbdxjXZpUGvz6hrlEMdx2kf6QAEsqMzWZ5ahkk3dZAuKzKn\nes9Q3VuDS3YRZ4xhRdpSUiL8R6I7Rjo51HGcQecQRsnIwsQS5ibM0SLSl4lLLifuD4vFcsxqtap7\n+mYBTUxraGio4dGXqzhyJrT37RdumccqFdPTVzM2xxD/c+xX9NqnVhYEUDwSjpMbQi4WMxkk/vOB\nlSSESA3whywrfO+JQ7T3hlioKMgY5x1AjPTvHGIZvY0TVcGj6CvmpnA4wL0RSKiHy7XZ67hjzq2X\nfJzpIisyT1Q9x4nu4P+WT1m2sS7zmsvUq6k4PE5+fvxxGocCV9mUBIkHS/+OksSpqWNd/aP81++P\nMTgaeBFxbKSB//PZpSRdxRaYGrPHJZcTt1gseywWy+5J/9UCmpuHhobGh57P3TQ35MKkm1flfuSF\nNMBL594IKKQBBMmDofgokj5wjrBRL/HVbQunJaQBRFHgH+5aFDxHV5DRF1QGFNIAFedCF+AIJKQB\nPD2ZuNoufep8R/Me6m2NoRvOEsc6K0IKaYAXal7B9gH6FG9vfD+okAbwKB5+f/pPeOSp1oXPvlMT\nVEiD1/bwuXfVzXpoaISDWu+cf530ZwUYBNSXY9LQ0ND4gDAbdfzz3WW8d6yV98tbfXKs5+bEsXl5\nNmVzkoMc4aPBkHNYVXU2MXIQ3bx9LJVvp7JmiDGHV9gYdCIrSlLZsjLnkqtJpsSZ+d59y3jnSDO7\nK9omLAsFGSm+E11aA2JU4EWMksccns1ZANwtFuThOHRpjUiTquMlmxOJ0EWEFH8X2N16gPwwLNFm\nkt2t+1W18yge9rcd5qb82fUWONPYz47jLZys68XlkomJMnDNvBTK9YdU7W9zDnGiu4qlqYvGP+vs\nH+VUvbrqhSfreukZGNOi0xoziioxbbVad1kslnXAcrxi+qDVatXSLDQ0NK4I9DqJG1fmsGVFNu29\nozhcHmIjDdOOnl6NnO6rmbKoLRCicYysvB7+bvN1dPZ70zGSYs1EmGbG2xggJtLAnRsLuX1tPp19\no/TbB3n8zKMoOkfIfR0dGahdxGjUSziCVIeUB1JxDqQSnyDz9U9aMOmMpEYk8+29AdciTaGyp1p1\n25lk2DVCXRhR8cqe6lkT07Ki8MxbZ9hd4WsjaBt28m51JcZ56qPilT3VPmK64myPaldwRYGK2l6u\n82ONp6ExXVS9+SwWy38ANwB78C69/oXFYvmr1Wr9wWx2TkNDQ2MmEQSBjEuMml6tODyhRerF7Y0G\niZxU9cUypoNeJ5KVEkUWUWxxreGtxh1B2yeZkmjuUB8FNhlEBIGgZcgFAT69oZS82Am/5XDKQtvd\n4V3bmcLhDq90tT3MeyAcXtxVO0VIjyOGV3Hy4n7agwyG/O7vnJ6VoYZGINSGETYBq61WqwxgsVh0\nwG5AE9OXgCwrVNX3eVe4SyJzc+KCTj21dA1T1z6ILCukJ0bMSEWz2eBci42WnmEEIC8thtwwKlOp\n5VB1J5V1vciKwpzMWDYtyWLE7qKyrpcxu5tIs57SwkRMBv+3uKwoVDf00T1gRycJFGfHBS0s0jHS\nSZ2tEY8ikxqRxJy4QhwuDydrexkZc2E26SgtSCTCFNx3GaB/yEF1Qx9Ol4f4aBMLChLQSf6jaC63\nTGVdL7Zhr3CZn5dAbBDrtto2Gy3nbc9yUqPJTw/ts+x0uXnm/eO0945i0IlcuziX5UW5tPeOcLbF\nhiwrpMSbKcmND7hqfMzh9l4Lu4tIk/fam43+r72iKJxu7KerfwxRFJiTFUt6YiTWpn72V3XgdMuk\nJURw8zW56CZVXnO6PFTW9TI44sRk0DG/IIGYCAP17YM0dXqjWlkpUUFdRGzDDk419OFweoiNMrKw\nIDFgdTe37OZ0Xw39dhsGSc/chDnEGWM5dqaL8nM9yIpCQXoM1y/L9tnP331o1EucaRqgs28UURQo\nyowNKuo7+0epaR7A7VFIjjMxLy8hYJlsu9Pt9z482zJAa88IoiCQlxY9RfTe/6tnEQzK+UIgMglx\nCgS3s/UhWh9FY8cQDR2DKEBWUhRFWeE5uDz15vvsPjUK8vl7RTfGk9+83befP3yLiZ8qietuXM7B\n/mNT7McAsqMyeGDBffzLvgrcqPMMj482cc8Nxfzqr5V+LR6Neon8LImH3zgAr+tAkDFF2ElYGEW/\nQ53tolEyUN5VyfzEuRgk/+8Ijyxzqr6P3kGH9/cgN46kWBW/B4pCeoL39+DY2Tae2VGB0yVj0Et8\n5roSREH0e6389sGp52RtD/PzEwKWL3eef+8NjXqfwwUFCURHBF6QerZlgNq2Qd45HDglJpzqh+CN\n6O1r9aaFZET5OnqoITrCQJ2tkfbhDgCyozPJiZkaqX7giSdR7JHeM+ocfOuW9czNnBio1bS38diO\n3TicMjqdwN2rlnJNUTF1tgbahjsQEMiOySQnOnAUfMBh40zfWZweF/GmWOYlWPw6uAC4ZDfVvWew\nOYYwSgZKEouJMUTz+P7Xqen1zkDkxKTxDxvuCHkNpvwGZsWRGsS7v2OkizpbAx5FJsWcRHF84Yy7\niNQONHCg/Qgu2UWKOYnNORsR0FFV18vAsAODXmJBfvDfwIbBJlqHvIO2zOj0oD7uA8MOTtV7f4vj\noowsKEhEFBXve98xgE7UUxxXSKI5tNZS5eZhsVj2Wa3WNRd9ttdqta4NufMsMZNuHv1DDsrPdjMy\n5iLSrKdsTjLx0bPnNasoCm8fbubdo830D02MsAUBFhYkctfGQjKTJ8qlWpv6eXF33ZTqbOmJEdyy\nKu9Ds3Dq6JkuXtlXT2v3iM/n+ekxbFtfwHyVFfOC8ebBRl7b1zBlWvbCMz35djYZJNYsTOfODYU+\nVkzvHWvhrUNN9A5OWFkJeCv13bWx0Ed01NsaeaX2b5wdqBv/TPFI6DpKcXSl4pq03sWgF1lZkspd\nm4r8FjPpGhjjzzvOUXGux8eqKybSwLVlmdyyOg/xfIU5t0fm1X31vF/e5lPiWhIFlhQn88lri3xS\nFMrPdvPKnnqaLvIPzk2LZuvafBYVTVVJHo+Hf39uF21tCr5evwqC5EYRXeCaeLmmxJlk0gFJAAAg\nAElEQVS58ZocNi7OHP9s1O7mL7tqOVDV4fOdGPUSqxakcdfGQh9R/f6JVv52sJHuAV8bMUkUptiX\nCQKUFiTy5dvn8+q+BnZXtPl4x0qigMkgTfGTzUmN4va1+T550L02O3/eeY7jNd0+54ky69lYlslt\na/LGBzSyIvNWw3vsbj3AkHPieno68nC3FiN7fEWGThJYX5rB1vUFvLDzHIeqO3FOKmGul0R0OmE8\nt/gCc3PiuGNDIYWZEwK0qXOIF96vpbq+z2faOjHGxI0rc3ympu1ONy/uqmNfZbtPVFWSBIw6iVGH\n73UpyPA+hz958w0E2YwyEgfKpH+LfhRT6T4EKXSUT0Agqe1jNLX4ts1MjuT2NfksU1Ex7/4fvos3\nFcP33gNANwJuEyAxxYcaBcEwxtates721+FW3CSZEliVsZy58V6bsF+/doqDpzpD9gHgU9fN4Ybl\n2bg9MkfPdHGwunNcKOr0DirrBkDWT+mnLtuKPr1B1TkuYNaZWZ2+nFsLtqA/L6oVReGtw01sP9ri\n9/fgE5uKfAZe1qZ+XtxV52v/JrhA8oDbOKWfhuJjSHE9qvrnrJ+PpzubuCgDm5dlc+PKnHHB5HJ7\neGl3PXtO+j6HOklk+dxkPrGpyEfkHD7dyWv7GmjtGZlyHn9MtiIMl3RDNo0H5uNREaDWSZC9uooO\nR4vP5znRWdxasIV5iRYe+M3vkAfSwXPx9y4jRPXzL59Yxw//+j6ewfjz98Z5BA9idB+6zBqk6Im0\nldzobG4t2OLjQtI92stLtW9Q2VPtM9iJNkSxIXMNW/I2jft/e2QPb9a/y562g4y4JlxuPAOJCAY7\nYoTvNZbHIkgUs/nPmz7v9xq8d6yFtw83+XiGC8C8vHju3FjkEwCrtzXxSu2bPr+BAEnmRG7I2cia\nTHVVPINR2VPNc6f/wpDr4u9fQBhKZvT0Yi6kbUmiQFlxMp/cVERirMnnGG/Uv0vzUKvPEbKjM7k5\nfzMLkyYqwPbYxvjzzlrKfX4PFCJzmtCnNeJg4hoLCMxPnMvHi25mYV7hpVnjWSyWX+It2LL9/Ec3\nAOesVutDIXcOfEwTUAX8B7AD+D3eq9UOfNZqtQZdljsTYnpwxMmz79ZcdEEnBMs9m4uJiQxvxKuG\nJ984zd7KwOVnzUYd37p7MXlpMZw428PDL1VOERqT2ba+gFtW5814P8Ph3aPNPL/9bMDtoiDwxdvm\nsaJk+hXY/rC9hu1HW0I3vIj89Gi+dXcZJoOOZ9+xsuN4a8C2RoPENz+5mKLMWE731fD4yad8fFkV\nj4TjzHKvEAlAemIE3/3MUh9B3d47wg+fO85QkNXmS4uTefDjC1AUhV/8pZLKuqkG/ReIizLwnc8s\nJSXOzK4TrTzzljVgzqAgwN/dVMLaUt8S3F/95TuMjQSbnFLAMApO3wjqTStzuGtTEaN2Fz/6Q3nQ\nAiDZKVF8+9NLiDDpeGHnOf52KHwTIINO9BGnahCAe2+0sGFxJl0DY/zw2WMM+Ik6XqC0MJGv37EQ\nQYAnqp7lRHeVz3ZX8xzc7QVMFXYTGPUiDld4/dTrRP7+jlLm5ydwrsXG//z5BI4g6QbXLcninhuK\nsTvd/Pj5curbw3RfiG2FkWSvX7IfvAKxPuRhPP2pOM+WBdx+9/Vz2HxRxH4y9//wPYJdS7hQuCVY\nG4Unv+M/v7e2zcZ/PXMsZB6tySDxk6+s9juj9OrBE7z8fg+Bcq8FwyjGRXsQhOBnURSmFP8oisvn\na4seQC/peeL1avZVdQTcP8Ko41t3l5GbFk15TTePvFzl+3sguADJd2A0CTG6F2PJkaB9BFDceuwn\nNkzMEgCr5qfxwC0l/z975x3e1nXm6feiAwQLSLB3sYBNvdqyLEuybMu927Fjx3Hi9OzMZJLdncmW\nzDyzSWZ3ys7MJp5MilNsJ06cuPciW7LVLFFUYxMpir03kASIdu/+AZIiiHbBIkryfZ8nTyzw4OLg\n4t57vnPO9/1+eH0i//TciYgGSNZEA3/12fVY4vW8/Uk7v3sv/HgQCnVqM7rC6O8RJw2oDMHGMe5z\nVfgGoudBq1Pb0RWeCfk3laDC2VSOOJhD2GtP7UTQeZCc4Xf9BJ0D7Yoa1An2gGM/Wv4AGzPW0jPR\nyz9X/zvjnvATjbWpK3m86mG/sdDJX1I71BDwd++wFXXSYNjrT5JAP57LP9/xzYDXn32nkXePhR9H\n9Vo133pgNSU5SdQPneXfTz4VpE0+mxvyd3BH0Z6wf4/G0d4anjrzbMQ24qQR18ltzL4XE806/urh\ndaRZTBzoOsKz9X9ECnPH+11E7+XqrI30DTv4wTPVc3ahJLQrTqGxdoXtg0lj5Jf3/NP8pPFsNpvK\nZrOpgG8BvweK8QfVvwK+Hem9MvjvwHS08LfAvzU0NGwHmoHHF3jsqNgn3Hz/6WMcre8LClR9osQn\n9X18/+lj2GW6fcnl41PdEQNp8G+Z//iF09gn3Pzk5TMRA2mAP+07x9kO+S5vi01b7xi/ixBIg39L\n6Wev1gWsvMT6GfMJpAFausf4w95mjtb3RQykAVxuHz964RTjLic/P/100EPE026LGEgDdA86+PWb\n9QGvPfnimYiBNMCxxn7ePdrBG4faIgbSACPjbn768hm6Byf4zVuNEYMGSYJfvVlP3/CF2fb/+eP+\nKIE0gABuI8w5+huH2zjZPMhv3z0b1UmvvW+c377byMnmwXkF0kDMgTT4e/ybtxrpHpzgpy+fiRhI\nA/7+HWrjg46PgwJp0WmKGkgDMQfS4E/jefLF04w73fzohVMRA2mA96o7OFrfxx/2NsceSAOCKzFs\nIA3g7SjBZ4+8gyQ543C3RLar/t17Z2dSb+by+N+/ReRzOUT0QBpAmFrdDqYoK5H7dhRHfLdWo+Jr\nd1aFTc168aNuIg6Rajey3IAAd292wL+bRlp4reUdPjrZHTGQBnC4vPz4xVOMTrj4ySshxgO1GDaQ\nBhDHUvB0RD4Xkk/tnxyJgc+Eg2d62H+ym5c+Oh/VSXRgdJJfvFZLa88Yz8UYSPv7YMA7ENkQSHLr\n8IS59rT5dahM9pB/m0YVN4o2rz7s3yd6hMiBNKCKt0cMpP39NOHpKAt4TZREnq77PcOTI/zs9NMR\nA2mA4/2n2NdxkLdb9wYF0r7xONSJ4QNp8E/gXOZ2fnn4zZnXjjX0RwykAVweHz964TRjk6HHwLm8\n3bqX2sGGiG3C4RW9/Kr2d1HbqQxOtIWBz+XRcTc/ebmWPkc/v234U9hAGvyuqL9t+CN9jgF+8nJt\nUDqX2toZMZAGcHiDnXYD+hjlO3gBDzAJ/Br4OvAXwHPAvCsVbDabDSgDXsN/1W4HXpn68yvA9fM9\ntlyeeaeRvuHIJ6dv2LnompTRLuRpBqa2pSNVmc/mPZnHXQrer+6QVUnt9Yl8WBM5mA3Hb+fxcJ7N\ngdM9vHVEXjA3Ou7mjyc/DLJ5lbwafFEe9tNUNw7MTBwa2obp6Je3ffn+sQ7er5b3WzZ32XnpoxZE\nGQO6T5T44PiFh0V9c/DKTmhU/tXpObx1pI3DddGNUAAO1/XJPveLiShJvPRRC81dkQfYad4/3sGH\n7cEyYp7WcqIHdvPH4fLy3PtNsm263/qknQNRArBQCMkdSJPmyI0kFe6GDXh7ctETGHTr1TqyhQom\nazdHDMjBH2OGnbhK0SZxScg/3+GHsJs25/HVO6vITQv8zgJQWWDhP39mLVUrUkK+973quqjfUW3t\nJooLt//zBFCFCHYPdB3hnWPnox8A6B+Z5A97m3HPnbCpXFH7CeDtKsbdvArREXguJAl8I1ZcdZsQ\nx0JPot496pcnlMOZ88O8cuC8bGWNgL5MWPCcW4mnzYboClTZkUQB72AmrtotiGOpuM6uDHq/oPah\nKz9MfpE7qF7DpNeQX+RCV3YkYhqTMLyGiNeeehzRHvqaCfo+4xa8Q4FtvZKPl5rfoHtCXgrSBx0f\nsa/zYPAfvAYEVfSzLAhwuPuCivF7x+RJOdon3Dz/ydGoAeQ0H3Z8LKvdXN48/57sfH51cvAzr6Xb\nziunjsg6hiiJvHzqMC3dweOBJn3hOvARn2oNDQ0BTwCbzba3oaFhx4I/Ff4Rf2D+2NS/42aldfQB\nmaHetFgMj7mobuyX1ba6sZ+RcRdJERLe5dI77KC1R/5qUs1ZeXlu4O+n1yeGLWRbSg7Xyguq/G17\nuXPbipg/I5Q9bCy4PD7ZQRXAqaFTMGfByjeSFrRyEw5RkvikrpcbNuXJDjqBAA1kOcRyjRyu6+X+\nncV8XN+CJIYucAmJL3jlrr51WPaA6fWJ1LWGNwJZSmI5P6NiH67J4B2BcEHGYnI8hn42z/NeEHx6\neb+ZpMLTVsnIWAJ/cc9Gxt0TGDUGbMkl/O3Pj4NP3jV6uK6Xx/YErs49/g8vANGKFGObuHz7J+/z\nD18Obdm9sSyNjWVpnOuy0z04gUolsCIzIWKhFcAz+xqAyKuPmhT5Exp1SnDwNOF1MOQ6D8hLfQt5\nLWu94JI3NvkGs/ANZqEyDyPoHYCAOJ6E5Ip8Ljr65eU8T3OiSf61PENSF9KIf6HC21OIt6cAVcIg\ngm4SRLV/t8R74XtKvtATCEHtw5FxmH+6879xpmWIiUkPZoOWisJkvnvwbxG8kRenJEfka1NIHEEa\nkiupJ+AbzkKTHPhMiUUmsd8ZeodSMMr/TYQ4/47C6LiL+jb5O9g1DSMgc6g+M9iA0+vEqIlNu/tw\nT7XstoJaRJXYhzgaWI9xvGEYZP4kNQ3DQGAfBcN4RPMnucQqCrrgPGWbzfYIcKChoaHVv0AdxJKb\nzB8/2x81dWIanyhR3djPznUL16Qcj7LNPxd3lBt/Nl6fhMPljbmqeaF4vD7Zq+dAQDFdLPh8F1fW\n3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VZo+SaPV+RoQx8dfeNTxVZmNtjSop775p4BfvPeaUbHPKjVAquKLXz2unWcah6kqXMU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\nX+Slmbl7e7Gs1Wm1SuDWqwupWpEStS34b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XM04blcLi5yGOfMiSMYNXL49Us9b265o+E6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd8ff9370f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize=(12, 9))\n",
    "sns.swarmplot(x=\"Education-Num\", y=\"Hours-per-week\", hue=\"Earnings-Raw\", data=adult[::50], size=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "X = np.arange(30).reshape((10, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X[:,1] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vt = VarianceThreshold()\n",
    "Xt = vt.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Xt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(vt.variances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = adult[[\"Age\", \"Education-Num\", \"Capital-gain\", \"Capital-loss\", \"Hours-per-week\"]].values\n",
    "y = (adult[\"Earnings-Raw\"] == ' >50K').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "transformer = SelectKBest(score_func=chi2, k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Xt_chi2 = transformer.fit_transform(X, y)\n",
    "print(transformer.scores_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr\n",
    "\n",
    "def multivariate_pearsonr(X, y):\n",
    "    scores, pvalues = [], []\n",
    "    for column in range(X.shape[1]):\n",
    "        cur_score, cur_p = pearsonr(X[:,column], y)\n",
    "        scores.append(abs(cur_score))\n",
    "        pvalues.append(cur_p)\n",
    "    return (np.array(scores), np.array(pvalues))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "transformer = SelectKBest(score_func=multivariate_pearsonr, k=3)\n",
    "Xt_pearson = transformer.fit_transform(X, y)\n",
    "print(transformer.scores_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "clf = DecisionTreeClassifier(random_state=14)\n",
    "scores_chi2 = cross_val_score(clf, Xt_chi2, y, scoring='accuracy')\n",
    "scores_pearson = cross_val_score(clf, Xt_pearson, y, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(\"Chi2 performance: {0:.3f}\".format(scores_chi2.mean()))\n",
    "print(\"Pearson performance: {0:.3f}\".format(scores_pearson.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin\n",
    "from sklearn.utils import as_float_array\n",
    "\n",
    "class MeanDiscrete(TransformerMixin):\n",
    "    def fit(self, X, y=None):\n",
    "        X = as_float_array(X)\n",
    "        self.mean = np.mean(X, axis=0)\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        X = as_float_array(X)\n",
    "        assert X.shape[1] == self.mean.shape[0]\n",
    "        return X > self.mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mean_discrete = MeanDiscrete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_mean = mean_discrete.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "from numpy.testing import assert_array_equal\n",
    "\n",
    "def test_meandiscrete():\n",
    "    X_test = np.array([[ 0,  2],\n",
    "                        [ 3,  5],\n",
    "                        [ 6,  8],\n",
    "                        [ 9, 11],\n",
    "                        [12, 14],\n",
    "                        [15, 17],\n",
    "                        [18, 20],\n",
    "                        [21, 23],\n",
    "                        [24, 26],\n",
    "                        [27, 29]])\n",
    "    mean_discrete = MeanDiscrete()\n",
    "    mean_discrete.fit(X_test)\n",
    "    assert_array_equal(mean_discrete.mean, np.array([13.5, 15.5]))\n",
    "    X_transformed = mean_discrete.transform(X_test)\n",
    "    X_expected = np.array([[ 0,  0],\n",
    "                            [ 0, 0],\n",
    "                            [ 0, 0],\n",
    "                            [ 0, 0],\n",
    "                            [ 0, 0],\n",
    "                            [ 1, 1],\n",
    "                            [ 1, 1],\n",
    "                            [ 1, 1],\n",
    "                            [ 1, 1],\n",
    "                            [ 1, 1]])\n",
    "    assert_array_equal(X_transformed, X_expected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "test_meandiscrete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipeline = Pipeline([('mean_discrete', MeanDiscrete()),\n",
    "                     ('classifier', DecisionTreeClassifier(random_state=14))])\n",
    "scores_mean_discrete = cross_val_score(pipeline, X, y, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print(\"Mean Discrete performance: {0:.3f}\".format(scores_mean_discrete.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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